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2. Combining complex networks and data mining: why and how

Author

Zanin, M., Papo, D., Sousa, P. A., Menasalvas, E., Nicchi, A., Kubik, E., and Boccaletti, S.

Subjects
Physics - Physics and Society, Computer Science - Databases, Computer Science - Information Retrieval, Computer Science - Social and Information Networks, Physics - Data Analysis, Statistics and Probability, 05C82, 62-07, 92C42
Abstract

The increasing power of computer technology does not dispense with the need to extract meaningful in- formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed., Comment: 58 pages, 19 figures

Published
2016
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3. Effect of Composition and Thickness on the Tribological Performance of Epoxy-MoS2 Composite Coatings in Reciprocating Block on Ring Tests.

Author

Zanin, M., Prieto, G., Tuckart, W., and Failla, M.

Subjects
COMPOSITE coating, MECHANICAL wear, PROTECTIVE coatings, TUBES, WEAR resistance
Abstract

Over the past two decades, there has been an increasing adoption of protective and lubricant coatings for their usage in threaded connections in the oil and gas industry to reduce both failed connections due to galling and environmental contamination due to lubricant spillage. In this work, the influence of composition and thickness on the tribological performance of MoS2-epoxy matrix composite coatings in reciprocating block-on-ring tests was studied. Epoxy resins with 2.5, 5, and 10 wt.% MoS2 were deposited on SAE 4140 steel blocks using a manual procedure, and after curing, hardness and thickness were measured. The tribological evaluation was performed using a block-on-ring test at low speed (30 mm/s), with reversing sliding direction and continuously varying loads between 0 and 5000 N. These conditions are similar to those encountered on the thread flanks during make-and-break operations in OCTG (Oil Country Tubular Goods) threaded connections. Wear surfaces were examined by optical and scanning electron microscopy (SEM). The wear resistance of coatings containing 2.5 and 5 wt.% of MoS2 strongly depends on their thickness, with a threshold around 70 μm. Coatings with lower thicknesses displayed constant wear rates, which decreased as the MoS2 concentration increased. On the other hand, coatings with thicknesses exceeding 70 μm showed an increase in wear rate proportionate to their thickness. In the case of coatings with 10% MoS2, the friction and wear were not influenced by coating thickness. These coatings exhibited the lowest average coefficient of friction (COF) values (0.06 ± 0.015) and wear rates (3.1 ± 1.3 × 10−8 mm3/N mm) compared to the other compositions tested. This performance is attributed to the formation of tenacious tribofilms on the surfaces. [ABSTRACT FROM AUTHOR]

Published
2024
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4. The structure and dynamics of multilayer networks

Author

Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., and Zanin, M.

Subjects
Physics - Physics and Society, Computer Science - Social and Information Networks, Nonlinear Sciences - Adaptation and Self-Organizing Systems
Abstract

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics., Comment: In Press, Accepted Manuscript, Physics Reports 2014

Published
2014
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5. Functional brain networks: great expectations, hard times, and the big leap forward

Author

Papo, D., Zanin, M., Pineda-Pardo, J. A., Boccaletti, S., and Buldú, J. M.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode., Comment: 12 pages, no figures. Philosophical Transactions of the Royal Society B, in press

Published
2014

7. Lung Protective Mechanical Ventilation in Severe Acute Brain Injured Patients: A Multicenter, Randomized Clinical Trial (PROLABI)

Author

Mascia, L, Fanelli, V, Mistretta, A, Filippini, M, Zanin, M, Berardino, M, Mazzeo, A, Caricato, A, Antonelli, M, Della Corte, F, Grossi, F, Munari, M, Caravello, M, Alessandri, F, Cavalli, I, Mezzapesa, M, Silvestri, L, Casartelli Liviero, M, Zanatta, P, Pelosi, P, Citerio, G, Filippini, C, Rucci, P, Rasulo, F, Tonetti, T, Mascia, Luciana, Fanelli, Vito, Mistretta, Alice, Filippini, Matteo, Zanin, Mattia, Berardino, Maurizio, Mazzeo, Anna Teresa, Caricato, Anselmo, Antonelli, Massimo, Della Corte, Francesco, Grossi, Francesca, Munari, Marina, Caravello, Massimiliano, Alessandri, Francesco, Cavalli, Irene, Mezzapesa, Mario, Silvestri, Lucia, Casartelli Liviero, Marilena, Zanatta, Paolo, Pelosi, Paolo, Citerio, Giuseppe, Filippini, Claudia, Rucci, Paola, Rasulo, Frank A., Tonetti, Tommaso, Mascia, L, Fanelli, V, Mistretta, A, Filippini, M, Zanin, M, Berardino, M, Mazzeo, A, Caricato, A, Antonelli, M, Della Corte, F, Grossi, F, Munari, M, Caravello, M, Alessandri, F, Cavalli, I, Mezzapesa, M, Silvestri, L, Casartelli Liviero, M, Zanatta, P, Pelosi, P, Citerio, G, Filippini, C, Rucci, P, Rasulo, F, Tonetti, T, Mascia, Luciana, Fanelli, Vito, Mistretta, Alice, Filippini, Matteo, Zanin, Mattia, Berardino, Maurizio, Mazzeo, Anna Teresa, Caricato, Anselmo, Antonelli, Massimo, Della Corte, Francesco, Grossi, Francesca, Munari, Marina, Caravello, Massimiliano, Alessandri, Francesco, Cavalli, Irene, Mezzapesa, Mario, Silvestri, Lucia, Casartelli Liviero, Marilena, Zanatta, Paolo, Pelosi, Paolo, Citerio, Giuseppe, Filippini, Claudia, Rucci, Paola, Rasulo, Frank A., and Tonetti, Tommaso

Abstract

Background: Lung protective strategies using low tidal volumes and moderate positive end expiratory pressures (PEEP) are considered best practice in critical care, but interventional trials have never been conducted in acutely brain-injured patients due to concerns about carbon dioxide control and effect of PEEP on cerebral hemodynamic. Methods: In this multicenter, open-label, controlled clinical trial 190 adult acute brain injured patients were assigned to receive either a lung-protective or a conventional ventilatory strategy. The primary outcome was a composite endpoint of death, ventilator dependency and ARDS at day 28. Neurological outcome was assessed at intensive care unit discharge by Oxford Handicap Scale and at six months by Glasgow Outcome Scale. Findings: The two study arms had similar characteristics at baseline. In the lung-protective and conventional strategy groups, using an intention-to-treat approach, the composite outcome at 28 days was 61.5% and 45.3% (RR 1.35; 95%CI 1.03-1.79; p=0.025). Mortality was 28.9% and 15.1% (RR 1.91; 95%CI 1.06-3.42; p=0.02), ventilator dependency was 42.3% and 27.9% (RR 1.52; 95%CI 1.01-2.28; p=0.039), and incidence of ARDS was 30.8% and 22.1% (RR 1.39; 95%CI 0.85-2.27; p=0.179) respectively. The trial was stopped after enrolling 190 subjects because of termination of funding. Interpretation: In acutely brain-injured patients without ARDS a lung-protective ventilatory strategy as compared to a conventional strategy did not reduce mortality, percentage of patients weaned from mechanical ventilation, incidence of ARDS and was not beneficial in terms of neurological outcomes. Due to the early termination, these preliminary results require confirmation in larger trials. Clinical trial registration available at www. Clinicaltrials: gov, ID: NCT01690819.

Published
2024

8. Anomalous Consistency in Mild Cognitive Impairment: a complex networks approach

Author

Martínez, J. H., Pastor, J. M., Ariza, P., Zanin, M., Papo, D., Maestú, F., Bajo, R., Boccaletti, S., and Buldú, J. M.

Subjects
Quantitative Biology - Neurons and Cognition, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physics - Biological Physics
Abstract

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as (parenclitic networks) is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affecting the MCI group and the focal points where MCI is specially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory., Comment: 13 pages, 6 figure, Extended version acepted to Chaos Solitons & Fractals. Elsevier, the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena 2014

Published
2013
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9. Explosive transitions to synchronization in networked phase oscillators

Author

Leyva, I., Sendiña-Nadal, I., Almendral, J., Navas, A., Zanin, M., Papo, D., Buldú, J. M., and Boccaletti, S.

Subjects
Condensed Matter - Disordered Systems and Neural Networks, Nonlinear Sciences - Chaotic Dynamics
Abstract

We introduce a condition for an ensemble of networked phase oscillators to feature an abrupt, first-order phase transition from an unsynchronized to a synchronized state. This condition is met in a very wide spectrum of situations, and for various oscillators' initial frequency distributions. We show that the occurrence of such transitions is always accompanied by the spontaneous emergence of frequency-degree correlations in random network architectures. We also discuss ways to relax the condition, and to further extend the possibility for the first-order transition to occur, and illustrate how to engineer magnetic-like states of synchronization. Our findings thus indicate how to search for abrupt transitions in real-world applications., Comment: (02Oct12) Submitted to Phys Rev Lett

Published
2012
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10. Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network

Author

Rad, A. Ajdari, Sendiña-Nadal, I., Papo, D., Zanin, M., Buldú, J. M., del Pozo, F., and Boccaletti, S.

Subjects
Nonlinear Sciences - Adaptation and Self-Organizing Systems, Condensed Matter - Statistical Mechanics, Physics - Biological Physics, Physics - Physics and Society
Abstract

We introduce an easily computable topological measure which locates the effective crossover between segregation and integration in a modular network. Segregation corresponds to the degree of network modularity, while integration is expressed in terms of the algebraic connectivity of an associated hyper-graph. The rigorous treatment of the simplified case of cliques of equal size that are gradually rewired until they become completely merged, allows us to show that this topological crossover can be made to coincide with a dynamical crossover from cluster to global synchronization of a system of coupled phase oscillators. The dynamical crossover is signaled by a peak in the product of the measures of intra-cluster and global synchronization, which we propose as a dynamical measure of complexity. This quantity is much easier to compute than the entropy (of the average frequencies of the oscillators), and displays a behavior which closely mimics that of the dynamical complexity index based on the latter. The proposed toplogical measure simultaneously provides information on the dynamical behavior, sheds light on the interplay between modularity vs total integration and shows how this affects the capability of the network to perform both local and distributed dynamical tasks.

Published
2012
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11. Forest damage inventory after the 'Vaia' storm in Italy

Author

Chirici G, Giannetti F, Travaglini D, Nocentini S, Francini S, D’Amico G, Calvo E, Fasolini D, Broll M, Maistrelli F, Tonner J, Pietrogiovanna M, Oberlechner K, Andriolo A, Comino R, Faidiga A, Pasutto I, Carraro G, Zen S, Contarin F, Alfonsi L, Wolynski A, Zanin M, Gagliano C, Tonolli S, Zoanetti R, Tonetti R, Cavalli R, Lingua E, Pirotti F, Grigolato S, Bellingeri D, Zini E, Gianelle D, Dalponte M, Pompei E, Stefani A, Motta R, Morresi D, Garbarino M, Alberti G, Valdevit F, Tomelleri E, Torresani M, Tonon G, Marchi M, Corona P, and Marchetti M

Subjects
Windstorms, North-Eastern Italy, Wind Damages, Forest Damage Inventory, Forestry, SD1-669.5
Abstract

On October 29, 2019, the Vaia storm hits the North-Eastern regions of Italy by wind gusts exceeding 200 km h-1. The forests in these regions have been seriously damaged. This contribution illustrates the methodology adopted in the emergency phase to estimate forest damages caused by Vaia storm, both in terms of damaged forest areas and growing stock volume of fallen trees. 494 Municipalities registered forest damages caused by Vaia, destroyed or intensely damaged forest stands amounted to about 42.500 ha, spread in Trentino Alto Adige, Veneto, Friuli Venezia Giulia, Lombardy and, only marginally, Piedmont and Valle d’Aosta. The growing stock volume of fallen trees was about 8.5 millions m3.

Published
2019
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12. The contributing external load factors to internal load during small-sided games in professional rugby union players

Author

Zanin, M., Azzalini, A., Ranaweera, J., Weaving, D., Darrall-Jones, J., Roe, Gregory, Zanin, M., Azzalini, A., Ranaweera, J., Weaving, D., Darrall-Jones, J., and Roe, Gregory

Abstract

Introduction: This study aimed to investigate which external load variables were associated with internal load during three small-sided games (SSG) in professional rugby union players. Methods: Forty professional rugby union players (22 forwards, 18 backs) competing in the English Gallagher Premiership were recruited. Three different SSGs were designed: one for backs, one for forwards, and one for both backs and forwards. General linear mixed-effects models were implemented with internal load as dependent variable quantified using Stagno's training impulse, and external load as independent variables quantified using total distance, high-speed (>61% top speed) running distance, average acceleration-deceleration, PlayerLoad™, PlayerLoad™ slow (<2 m·s −1), number of get-ups, number of first-man-to-ruck. Results: Internal load was associated with different external load variables dependent on SSG design. When backs and forwards were included in the same SSG, internal load differed between positional groups (MLE = −121.94, SE = 29.03, t = −4.20). Discussion: Based on the SSGs investigated, practitioners should manipulate different constraints to elicit a certain internal load in their players based on the specific SSG design. Furthermore, the potential effect of playing position on internal load should be taken into account in the process of SSG design when both backs and forwards are included.

Published
2023

13. Differences and variability of physical and technical characteristics among rugby union small-sided games performed within a preseason

Author

Zanin, M, Ranaweera, J, Darrall-Jones, J, Roe, G, Zanin, M, Ranaweera, J, Darrall-Jones, J, and Roe, G

Abstract

This study aimed to investigate the differences in physical and technical characteristics among three specific rugby union small-sided games (SSG) and to examine the variability of these characteristics over three weeks within a preseason of a professional rugby union club. Eighteen backs and 22 forwards were recruited for the study. The three SSG designs were: backs only (SSG-B), forwards only (SSG-F) and both backs and forwards (SSG-BF). Physical characteristics were quantified using external (e.g., total distance covered [m·min−1]) and internal (i.e., Stagno's training impulse [AU·min−1]) load measures. Technical characteristics were quantified using the number of rucks, successful passes, unsuccessful passes, line breaks and tries per minute. The SSG-BF produced a greater high speed (>61%) running distance covered in comparison with SSG-B (1.97 vs. 1.32 m·min−1) and SSG-F (1.26 vs. 0.94 m·min−1), and more successful passes (9.47 vs. 9.36 count·min−1) and line breaks (0.98 vs. 0.65 count·min−1) than SSG-F. Conversely, all the other physical and technical characteristics were higher in SSG-B and SSG-F. All the physical and technical characteristics, except high speed (>61%) distance covered in forwards and unsuccessful passes and tries per minute, changed over days showing either a linear or quadratic pattern. Based on these findings, practitioners may implement position-specific SSG (i.e., SSG-B and SSG-F) to expose players to greater physical and technical characteristics. Furthermore, if SSGs were to be repeated across multiple days, practitioners should be aware of the possible variability in physical and technical characteristics due to potential adaptations to the constraints or the onset of fatigue.

Published
2023

14. The contributing external load factors to internal load during small-sided games in professional rugby union players

Author

Zanin, M, Azzalini, A, Ranaweera, J, Weaving, D, Darrall-Jones, J, Roe, G, Zanin, M, Azzalini, A, Ranaweera, J, Weaving, D, Darrall-Jones, J, and Roe, G

Abstract

INTRODUCTION: This study aimed to investigate which external load variables were associated with internal load during three small-sided games (SSG) in professional rugby union players. METHODS: Forty professional rugby union players (22 forwards, 18 backs) competing in the English Gallagher Premiership were recruited. Three different SSGs were designed: one for backs, one for forwards, and one for both backs and forwards. General linear mixed-effects models were implemented with internal load as dependent variable quantified using Stagno's training impulse, and external load as independent variables quantified using total distance, high-speed (>61% top speed) running distance, average acceleration-deceleration, PlayerLoad™, PlayerLoad™ slow (<2 m·s-1), number of get-ups, number of first-man-to-ruck. RESULTS: Internal load was associated with different external load variables dependent on SSG design. When backs and forwards were included in the same SSG, internal load differed between positional groups (MLE = -121.94, SE = 29.03, t = -4.20). DISCUSSION: Based on the SSGs investigated, practitioners should manipulate different constraints to elicit a certain internal load in their players based on the specific SSG design. Furthermore, the potential effect of playing position on internal load should be taken into account in the process of SSG design when both backs and forwards are included.

Published
2023

19. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. 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P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019
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20. Using consensus methods to standardise judgement-based guidelines required for player management decision-making processes: a case study in professional rugby union.

Author

Ranaweera, JS, Zanin, M, Weaving, D, Roe, G, Ranaweera, JS, Zanin, M, Weaving, D, and Roe, G

Abstract

Standards are pivotal for generating the evidence required to manage players in professional sport environments like rugby union. Resultantly, using a three-step qualitative approach, this study aimed to formulate a consensus as a subjective standard for evidence generation pertaining to player management. The consensus statement intended to identify evidence on peaks/troughs in player external training loads using Global Positioning System (GPS)-based information in the High-Performance Unit (HPU) of a Gallagher Premiership rugby union club. Initially, a systematic review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework was conducted to unravel the factors considered (literature-based cues) when identifying peaks/troughs in player external training loads using GPS information. Next, thematic analysis conducted on the data obtained from 7 semi-structured interviews with HPU staff highlighted that they consider 6 factors with 38 elements (practitioner-based cues) during player external training load management. Thereafter, guided by the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument and by utilising selected elements representing 4/6 factors (healthy player, GPS information, longitudinal durations and practitioner judgements on information), a consensus among practitioners for identifying peaks/troughs in player external training loads was developed with the participation of five HPU members using the nominal group technique (NGT). Practitioners reached an agreement with regard to 12 indicators to subjectively identify peaks/troughs in player external training loads within the considered environment.

Published
2022

21. Identifying the Current State and Improvement Opportunities in the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union.

Author

Ranaweera, J, Weaving, D, Zanin, M, Roe, G, Ranaweera, J, Weaving, D, Zanin, M, and Roe, G

Abstract

In sporting environments, the knowledge necessary to manage athletes is built on information flows associated with player management processes. In current literature, there are limited case studies available to illustrate how such information flows are optimized. Hence, as the first step of an optimization project, this study aimed to evaluate the current state and the improvement opportunities in the player management information flow executed within the High-Performance Unit (HPU) at a professional rugby union club in England. Guided by a Business Process Management framework, elicitation of the current process architecture illustrated the existence of 18 process units and two core process value chains relating to player management. From the identified processes, the HPU management team prioritized 7 processes for optimization. In-depth details on the current state (As-Is) of the selected processes were extracted from semi-structured, interview-based process discovery and were modeled using Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) standards. Results were presented for current issues in the information flow of the daily training load management process, identified through a thematic analysis conducted on the data obtained mainly from focus group discussions with the main stakeholders (physiotherapists, strength and conditioning coaches, and HPU management team) of the process. Specifically, the current state player management information flow in the HPU had issues relating to knowledge creation and process flexibility. Therefore, the results illustrate that requirements for information flow optimization within the considered environment exist in the transition from data to knowledge during the execution of player management decision-making processes.

Published
2022

22. Digitally Optimizing the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union.

Author

Ranaweera, J, Weaving, D, Zanin, M, Pickard, MC, Roe, G, Ranaweera, J, Weaving, D, Zanin, M, Pickard, MC, and Roe, G

Abstract

Practical case studies elaborating end-to-end attempts to improve the quality of information flows associated with athlete management processes are scarce in the current sport literature. Therefore, guided by a Business Process Management (BPM) approach, the current study presents the outcomes from a case study to optimize the quality of strength and conditioning (S&C) information flow in the performance department of a professional rugby union club. Initially, the S&C information flow was redesigned using integral technology, activity elimination and activity automation redesign heuristics. Utilizing the Lean Startup framework, the redesigned information flow was digitally transformed by designing data collection, management and visualization systems. Statistical tests used to assess the usability of the data collection systems against industry benchmarks using the System Usability Scale (SUS) administered to 55 players highlighted that its usability (mean SUS score of 87.6 ± 10.76) was well above average industry benchmarks of similar systems (Grade A from SUS scale). In the data visualization system, 14 minor usability problems were identified from 9 cognitive walkthroughs conducted with the High-Performance Unit (HPU) staff. Pre-post optimization information quality was subjectively assessed by administering a standardized questionnaire to the HPU members. The results indicated positive improvements in all of the information quality dimensions (with major improvements to the accessibility) relating to the S&C information flow. Additionally, the methods utilized in the study would be especially beneficial for sporting environments requiring cost effective and easily adoptable information flow digitization initiatives which need to be implemented by its internal staff members.

Published
2022

23. Designing a small-sided game to elicit attacking tactical behaviour in professional rugby union forwards.

Author

Zanin, M, Azzalini, A, Ranaweera, J, Till, K, Darrall-Jones, J, Roe, G, Zanin, M, Azzalini, A, Ranaweera, J, Till, K, Darrall-Jones, J, and Roe, G

Abstract

This study aimed to investigate the consistency of attacking tactical and technical behaviour, and physical characteristics, over multiple bouts, and variability across days, of a specific rugby union forwards small-sided game (SSG). Data was collected from 21 professional rugby union forwards during four training sessions. The SSG, consisting of five bouts of work (150-s) interspersed by passive recovery (75-s), aimed to elicit specific attacking tactical behaviour. Tactical behaviour (i.e., regularity of attacking shape [entropy]), and technical (e.g., passes) and physical (e.g., total distance) characteristics were quantified. Results showed that technical characteristics remained consistent, whereas the regularity of width of the attacking shape and two physical characteristics (i.e., total distance, training impulse) varied across bouts. However, these effects had limited practical significance. Technical characteristics were consistent across days, but minimal variability was observed for tactical behaviour and physical characteristics, as shown by their small random effects with 95% profile likelihood confidence intervals (PLCI) including zero (e.g., SD[95%PLCI] = 0.03[0.00, 0.06]). Consequently, consistency of stimulus over bouts and days is achievable for the majority of the variables investigated, thus supporting the use of SSG to elicit consistent attacking behaviour, but also technical and physical characteristics in rugby union forwards.

Published
2022

30. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

31. The AIMe registry for artificial intelligence in biomedical research

Author

Matschinske, J., Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., Blumenthal, D.B., Matschinske, J., Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., and Blumenthal, D.B.

Abstract

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Published
2021

32. A systematic review of small sided games within rugby: Acute and chronic effects of constraints manipulation.

Author

Zanin, M, Ranaweera, J, Darrall-Jones, J, Weaving, D, Till, K, Roe, G, Zanin, M, Ranaweera, J, Darrall-Jones, J, Weaving, D, Till, K, and Roe, G

Abstract

Small-sided games is a commonly used training method to develop technical, tactical and physical qualities concurrently. However, a review of small-sided games in rugby football codes (e.g. rugby union, rugby league) is not available. This systematic review aims to investigate the acute responses and chronic adaptations of small-sided games within rugby football codes considering the constraints applied. Four electronical databases were systematically searched until August 2020. Acute and chronic studies investigating rugby football codes small-sided games, with healthy amateur and professional athletes were included. Twenty studies were eventually included: 4 acute and 1 chronic in rugby union, 13 acute and 2 chronic in rugby league. Acute studies investigated task and individual constraints. Chronic studies showed that small-sided games would be an effective training method to improve physical performance. Current research in rugby football codes is heavily biased towards investigating how manipulating constraints can affect the physical characteristics of small-sided games, with limited literature investigating the effect on technical skills, and no studies investigating tactical behaviour. Future research is needed to evidence the effects of constraint manipulation on technical and tactical behaviour of rugby football players in small-sided games, in addition to physical characteristics.

Published
2021

33. Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sports

Author

Ranaweera, J, Zanin, M, Weaving, D, Withanage, C, Roe, G, Ranaweera, J, Zanin, M, Weaving, D, Withanage, C, and Roe, G

Abstract

Typical player management processes focus on managing an athlete's physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.

Published
2021

34. Assessment of dielectric behavior of recycled/virgin high density polyethylene blends

Author

Cruz, S.A. and Zanin, M.

Subjects
Dielectrics -- Research, Polyethylene -- Research, Semiconductor device, Business, Electronics, Electronics and electrical industries
Abstract

In this work, high density polyethylene (HDPE) incorporation in virgin resin was assessed as to its electrical performance with the use of volumetric resistivity, dielectric strength, and stress tracking measurements. HDPE post-consumer packaging was collected and submitted to a basic process of plastic recovering; washing, grinding, and drying. Formulations containing 0, 25, 50, 75, and 100% of recycled material in the virgin resin were reprocessed by extrusion and injection molding with stabilization. Dielectric strength test results were analyzed by the statistical distribution of Weibull, and the maximum likelihood method. The degree of crystallinity was measured by X-ray diffraction. Atomic absorption spectroscopy was also done to identify metallic residue present in the samples. From the results analyzed, it can be concluded that the HDPE derived from post-consumer packaging can be considered in electrical insulation systems for low voltage (up to 600 V) containing as much as 75% incorporated virgin resin. Index Terms--Recycling, post-consumer, polyethylene, dielectric properties, dielectric strength, volumetric resistivity, stress tracking.

Published
2004

35. Influence of Additives on the Dielectric Strength of High-density Polyethylene

Author

Ueki, M.M. and Zanin, M.

Subjects
Electric insulators -- Research, Dielectrics -- Research, Electrical engineering -- Research, Business, Electronics, Electronics and electrical industries
Abstract

In this study, we present the results of the influence of chemical additives (antioxidant and UV stabilizer) and pigments (titanium dioxide and carbon black) on the short-term dielectric breakdown test of high-density polyethylene (HDPE). These additives and pigments are commonly added to polyolefins, which are used as insulating material for medium voltage cables. The incorporation was performed in a single screw extruder and thin films specimens were obtained by hot compression from extruded materials. For the dielectric breakdown test, an automated system has been used. A voltage ramp of 500 V/s was applied to specimens immersed in a silicon oil bath at room temperature. The degree of crystallinity and chemical modification of the formulations were evaluated by X-ray diffraction and Fourier transform infrared (FTIR), respectively. The dielectric breakdown results have been analyzed by a Weibull distribution. The shape and scale parameters of this distribution have been obtained by a graphic and maximum likelihood method. These results showed that the carbon black is the component that affects the dielectric strength, that the (beta) shape parameter from the graphic method can be used to evaluate additive mixing conditions, and that the weakest point for formation of the rupture channel is on the carbon black agglomerate.

Published
1999

36. Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Author

Zanin, M, Chorbev, I, Stres, B, Stalidzans, E, Vera, J, Tieri, P, Castiglione, F, Groen, D, Zheng, H, Baumbach, J, Schmid, JA, Basilio, J, Klimek, P, Debeljak, N, Rozman, D, and Schmidt, HHHW

Subjects
modelling, computing, Systems Analysis, Data Science, Humans, Computer Simulation, data science, systems medicine
Abstract

© 2017 The Author 2017. Published by Oxford University Press. Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology COST) Action CA15120 Open Multiscale Systems Medicine OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine. Austrian Science Fund: Special Research Program SFB-F54. The European Cooperation in Science and Technology (COST) Action CA15120 OpenMultiMed (http://openmultimed.net).

Published
2019
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37. Contrasting chaotic with stochastic dynamics via ordinal transition networks

Author

Olivares, F., Zanin, M., Zunino, Luciano José, and Pérez, D.G.

Subjects
Dynamical systems theory, Series (mathematics), Plane (geometry), Computer science, Applied Mathematics, Numerical analysis, NONLINEAR DYNAMICS, Chaotic, General Physics and Astronomy, Statistical and Nonlinear Physics, purl.org/becyt/ford/1.3 [https], Complex network, 01 natural sciences, STOCHASTIC PROCESSES, 010305 fluids & plasmas, purl.org/becyt/ford/1 [https], Position (vector), 0103 physical sciences, CHAOS, 010306 general physics, Representation (mathematics), Algorithm, Mathematical Physics
Abstract

We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts. We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts. Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile Fil: Zanin, M.. Universidad Politécnica de Madrid; España Fil: Zanin, M.. Universidad Politécnica de Madrid; España Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile

Published
2020
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38. QUALITY OF DOCUMENTATION PROCESSES AT THE UNIVERSITY: THE IMPLEMENTATION OF THE PRINCIPLE OF «ONE WINDOW»

Author

Dorozhkin, E. M. and Zanin, M. V.

Subjects
ПРИНЦИП «ОДНОГО ОКНА», ДОКУМЕНТАЦИОННОЕ СОПРОВОЖДЕНИЕ ОБУЧАЮЩИХСЯ, PRINCIPLE OF «ONE WINDOW», DOCUMENTATION ESCORT OF STUDENTS
Abstract

В статье раскрываются вопросы обеспечения и повышения качества документационного, административного и информационного сопровождения обучающихся вуза The article reveals the questions ensure and improve the quality of documentation, administrative and information support of students of the University

Published
2019

39. Cross-CPP - An ecosystem for provisioning, consolidating, and analysing big data from cyber-physical products

Author

Correia, A., Wolff, C., Herrmann, E. A., Corral, V., Kachelmann, M., Zanin, M., Menasalvas, E., Delong, R., and Pavel Smrz

Subjects
Informática
Abstract

It is expected that with the increasing number of connected sensors and actuators within mass products, the large spectrum of sensor data coming from high volume products in various industrial sectors (vehicles, smart home devices, etc.) will rise in short-term. This enormous amount of data continuously generated by CPPs will represent (1) a new information resource to create new value, allowing the improvement of existing services or the establishment of diverse new cross-sectorial services, by combining data streams from various sources, and (2) a major big data-driven business potential, not only for the manufacturers of Cyber Physical Products (CPP), but in particular also for cross-sectorial industries as well as various organisations with interdisciplinary applications. In spite of major advances in the field, several challenges still hinder the use of these data, like the lack of, or only few, CPP ecosystems that are in the best-case manufacturer specific and not open for external companies interested in using such data. We present here a solution that envisions to establish a CPP Big Data Ecosystem to bring to the outside world CPP data from various industrial sectors, brand independent, allowing for external service providers that use CPP data from this unique CPP data access point (as well as from other sources) to develop cross-sectorial services.

Published
2019

40. Stima dei danni della tempesta 'Vaia' alle foreste in Italia

Author

Chirici, G, Giannetti, F, Travaglini, D, Nocentini, S, Francini, S, D’Amico, G, Calvo, E, Fasolini, D, Broll, M, Maistrelli, F, Tonner, J, Pietrogiovanna, M, Oberlechner, K, Andriolo, A, Comino, R, Faidiga, A, Pasutto, I, Carraro, G, Zen, S, Contarin, F, Alfonsi, L, Wolynski, A, Zanin, M, Gagliano, C, Tonolli, S, Zoanetti, R, Tonetti, R, Cavalli, R, Lingua, E, Pirotti, F, Grigolato, S, Bellingeri, D, Zini, E, Gianelle, D, Dalponte, M, Pompei, E, Stefani, A, Motta, R, Morresi, D, Garbarino, M, Alberti, G, Valdevit, F, Tomelleri, E, Torresani, M, Tonon, G, Marchi, M, Corona, P, and Marchetti, M

Subjects
Windstorms, North-Eastern Italy, Wind Damages, Forest Damage Inventory, North-eastern Italy, Settore AGR/05 - ASSESTAMENTO FORESTALE E SELVICOLTURA, Forest Damage Inventory, Windstorms, Wind Damages
Published
2019

41. Качество документационных процессов в вузе: реализация принципа «одного окна»

Author

Дорожкин, Е. М., Занин, М. В., Dorozhkin, E. M., Zanin, M. V., Дорожкин, Е. М., Занин, М. В., Dorozhkin, E. M., and Zanin, M. V.

Abstract

В статье раскрываются вопросы обеспечения и повышения качества документационного, административного и информационного сопровождения обучающихся вуза, The article reveals the questions ensure and improve the quality of documentation, administrative and information support of students of the University

Published
2019

43. Editorial: Spatially Embedded Complex Networks

Author

Strano E, Zanin M, Estrada E, LILLO, FABRIZIO, Strano E, Zanin M, Estrada E, Lillo F, Strano, E, Zanin, M, Estrada, E, and Lillo, Fabrizio

Subjects
Assente
Abstract

Assente

Published
2013

44. The ACE Brain

Author

Zanin, M., Papo, D., Suckling, J., and Di Ieva, A.

Subjects
functional networks, Boolean modeling, Thesaurus (information retrieval), Opinion, Computer science, 05 social sciences, Neuroscience (miscellaneous), MEDLINE, EDVAC, Data science, 050105 experimental psychology, NO, Functional networks, World Wide Web, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, 0501 psychology and cognitive sciences, complex network theory, 030217 neurology & neurosurgery, ACE, Neuroscience
Published
2016

46. Air Transport Network: a short review

Author

Zanin, M, LILLO, Fabrizio, Zanin, M, and Lillo, F

Subjects
Networks, complex systems, transport, air traffic management
Published
2013

47. On Demand Data Analysis and Filtering for Inaccurate Flight Trajectories

Author

Zanin, M., Perez, D., Chatterjee, K., Kolovos, D. S., Paige, R. F., Horst, A., and Bernhard Rumpe

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering
Abstract

This paper reports on work performed in the context of the COMPASS SESAR-JU WP-E project, on developing an approach for identifying and filtering inaccurate trajectories (ghost flights) in historical data originating from the EUROCONTROL-operated Demand Data Repository (DDR)., 5 pages, 5 figures. D. Schaefer (ed) Proceedings of the SESAR Innovation Days (2011) EUROCONTROL, November 29 - December 1, 2011, Toulouse, France

Published
2014

49. METHOD FOR TOMOGRAPHIC RECONSTUCTION OF A SAMPLE Patent AU2014902582 - filed

Author

Schena, Gianni, Piller, Marzio, Zanin, M., Schena, Gianni, Piller, Marzio, and M., Zanin

Subjects
chemical composition, X-ray computed microtomography, Heavy mineral analysis
Abstract

The present invention relates to tomographic reconstruction of a sample. In a particular form the present invention relates to reconstruction of a micro-tomographic scan of a mineral sample to determine sample composition.

Published
2014

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