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2. iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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5. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023

Author

Faggioli, Guglielmo, Guazzo, Alessandro, Marchesin, Stefano, Menotti, Laura, Trescato, Isotta, Aidos, Helena, Bergamaschi, Roberto, Birolo, Giovanni, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, García Dominguez, Jose Manuel, Gromicho, Marta, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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6. iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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13. Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

Author

Tavazzi, Erica, Longato, Enrico, Vettoretti, Martina, Aidos, Helena, Trescato, Isotta, Roversi, Chiara, Martins, Andreia S., Castanho, Eduardo N., Branco, Ruben, Soares, Diogo F., Guazzo, Alessandro, Birolo, Giovanni, Pala, Daniele, Bosoni, Pietro, Chiò, Adriano, Manera, Umberto, de Carvalho, Mamede, Miranda, Bruno, Gromicho, Marta, Alves, Inês, Bellazzi, Riccardo, Dagliati, Arianna, Fariselli, Piero, Madeira, Sara C., and Di Camillo, Barbara

Published
2023
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16. Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors

Author

Pereira, Telma, Pires, Sofia, Gromicho, Marta, Pinto, Susana, de Carvalho, Mamede, and Madeira, Sara C.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clinical translation of such models is limited by the lack of insight 1) on the risk of error for predictions at patient-level, and 2) on the most adequate time to administer the non-invasive ventilation. To address these issues, we combine Conformal Prediction (a machine learning framework that complements predictions with confidence measures) and a mixture experts into a prognostic model which not only predicts whether an ALS patient will suffer from respiratory insufficiency but also the most likely time window of occurrence, at a given reliability level. Promising results were obtained, with near 80% of predictions being correctly identified.

Published
2019

17. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022

Author

Guazzo, Alessandro, Trescato, Isotta, Longato, Enrico, Hazizaj, Enidia, Dosso, Dennis, Faggioli, Guglielmo, Di Nunzio, Giorgio Maria, Silvello, Gianmaria, Vettoretti, Martina, Tavazzi, Erica, Roversi, Chiara, Fariselli, Piero, Madeira, Sara C., de Carvalho, Mamede, Gromicho, Marta, Chiò, Adriano, Manera, Umberto, Dagliati, Arianna, Birolo, Giovanni, Aidos, Helena, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barrón-Cedeño, Alberto, editor, Da San Martino, Giovanni, editor, Degli Esposti, Mirko, editor, Sebastiani, Fabrizio, editor, Macdonald, Craig, editor, Pasi, Gabriella, editor, Hanbury, Allan, editor, Potthast, Martin, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2022
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18. Whole genome sequencing analysis reveals post-zygotic mutation variability in monozygotic twins discordant for amyotrophic lateral sclerosis

Author

Tazelaar, Gijs H.P., Hop, Paul J., Seelen, Meinie, van Vugt, Joke J.F.A., van Rheenen, Wouter, Kool, Lindy, van Eijk, Kristel R., Gijzen, Marleen, Dooijes, Dennis, Moisse, Matthieu, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Canosa, Antonio, Nordin, Angelica, Pardina, Jesus S. Mora, Ravits, John, Al-Chalabi, Ammar, Chio, Adriano, McLaughlin, Russell L., Hardiman, Orla, Van Damme, Philip, de Carvalho, Mamede, Neuwirth, Christoph, Weber, Markus, Andersen, Peter M, van den Berg, Leonard H., Veldink, Jan H., and van Es, Michael A.

Published
2023
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26. Structural variation analysis of 6,500 whole genome sequences in amyotrophic lateral sclerosis

Author

Al Khleifat, Ahmad, Iacoangeli, Alfredo, van Vugt, Joke J. F. A., Bowles, Harry, Moisse, Matthieu, Zwamborn, Ramona A. J., van der Spek, Rick A. A., Shatunov, Aleksey, Cooper-Knock, Johnathan, Topp, Simon, Byrne, Ross, Gellera, Cinzia, López, Victoria, Jones, Ashley R., Opie-Martin, Sarah, Vural, Atay, Campos, Yolanda, van Rheenen, Wouter, Kenna, Brendan, Van Eijk, Kristel R., Kenna, Kevin, Weber, Markus, Smith, Bradley, Fogh, Isabella, Silani, Vincenzo, Morrison, Karen E., Dobson, Richard, van Es, Michael A., McLaughlin, Russell L., Vourc’h, Patrick, Chio, Adriano, Corcia, Philippe, de Carvalho, Mamede, Gotkine, Marc, Panades, Monica P., Mora, Jesus S., Shaw, Pamela J., Landers, John E., Glass, Jonathan D., Shaw, Christopher E., Basak, Nazli, Hardiman, Orla, Robberecht, Wim, Van Damme, Philip, van den Berg, Leonard H., Veldink, Jan H., and Al-Chalabi, Ammar

Published
2022
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27. Towards Triclustering-Based Classification of Three-Way Clinical Data: A Case Study on Predicting Non-invasive Ventilation in ALS

Author

Soares, Diogo, Henriques, Rui, Gromicho, Marta, Pinto, Susana, de Carvalho, Mamede, Madeira, Sara C., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Panuccio, Gabriella, editor, Rocha, Miguel, editor, Fdez-Riverola, Florentino, editor, Mohamad, Mohd Saberi, editor, and Casado-Vara, Roberto, editor

Published
2021
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28. Predictive Medicine Using Interpretable Recurrent Neural Networks

Author

Ferreira, André, Madeira, Sara C., Gromicho, Marta, de Carvalho, Mamede, Vinga, Susana, Carvalho, Alexandra M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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31. An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology.

Author

Faggioli, Guglielmo, Menotti, Laura, Marchesin, Stefano, Chió, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Gromicho, Marta, Manera, Umberto, Tavazzi, Eleonora, Di Nunzio, Giorgio Maria, Silvello, Gianmaria, and Ferro, Nicola

Subjects
KNOWLEDGE base, AMYOTROPHIC lateral sclerosis, MEDICAL care, MODULAR construction, OPEN scholarship
Abstract

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability. Database URL https://zenodo.org/records/7886998. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.

Author

M. Amaral, Daniela, Soares, Diogo F., Gromicho, Marta, de Carvalho, Mamede, Madeira, Sara C., Tomás, Pedro, and Aidos, Helena

Subjects
AMYOTROPHIC lateral sclerosis, DISEASE progression, MOTOR neuron diseases, NEURODEGENERATION, HIERARCHICAL clustering (Cluster analysis)
Abstract

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups. The authors proposed ClusTric, a temporal stratification approach to find disease progression groups. Applied to Amyotrophic Lateral Sclerosis, the method identifies four progression groups with distinguished characteristics. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Mutations in the tail and rod domains of the neurofilament heavy‐chain gene increase the risk of ALS.

Author

Marriott, Heather, Spargo, Thomas P., Al Khleifat, Ahmad, Andersen, Peter M, Başak, Nazli A., Cooper‐Knock, Johnathan, Corcia, Philippe, Couratier, Philippe, de Carvalho, Mamede, Drory, Vivian, Gotkine, Marc, Landers, John E., McLaughlin, Russell, Pardina, Jesús S. Mora, Morrison, Karen E., Pinto, Susana, Shaw, Christopher E., Shaw, Pamela J., Silani, Vincenzo, and Ticozzi, Nicola

Subjects
GENETIC testing, CYTOPLASMIC filaments, WHOLE genome sequencing, MISSENSE mutation, GENETIC mutation, NEMALINE myopathy
Abstract

Objective: Neurofilament heavy‐chain gene (NEFH) variants are associated with multiple neurodegenerative diseases, however, their relationship with ALS has not been robustly explored. Still, NEFH is commonly included in genetic screening panels worldwide. We therefore aimed to determine if NEFH variants modify ALS risk. Methods: Genetic data of 11,130 people with ALS and 7,416 controls from the literature and Project MinE were analysed. We performed meta‐analyses of published case–control studies reporting NEFH variants, and variant analysis of NEFH in Project MinE whole‐genome sequencing data. Results: Fixed‐effects meta‐analysis found that rare (MAF <1%) missense variants in the tail domain of NEFH increase ALS risk (OR 4.55, 95% CI 2.13–9.71, p < 0.0001). In Project MinE, ultrarare NEFH variants increased ALS risk (OR 1.37 95% CI 1.14–1.63, p = 0.0007), with rod domain variants (mostly intronic) appearing to drive the association (OR 1.45 95% CI 1.18–1.77, pMadsen–Browning = 0.0007, pSKAT‐O = 0.003). While in the tail domain, ultrarare (MAF <0.1%) pathogenic missense variants were also associated with higher risk of ALS (OR 1.94, 95% CI 0.86–4.37, pMadsen–Browning = 0.039), supporting the meta‐analysis results. Finally, several tail in‐frame deletions were also found to affect disease risk, however, both protective and pathogenic deletions were found in this domain, highlighting an intricate architecture that requires further investigation. Interpretation: We showed that NEFH tail missense and in‐frame deletion variants, and intronic rod variants are risk factors for ALS. However, they are not variants of large effect, and their functional impact needs to be clarified in further studies. Therefore, their inclusion in routine genetic screening panels should be reconsidered. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Creatine Kinase and Respiratory Decline in Amyotrophic Lateral Sclerosis.

Author

Correia, João Pedro, Gromicho, Marta, Pronto-Laborinho, Ana Catarina, Oliveira Santos, Miguel, and de Carvalho, Mamede

Subjects
AMYOTROPHIC lateral sclerosis, CREATINE kinase, ACTION potentials, PHRENIC nerve, VITAL capacity (Respiration)
Abstract

Respiratory dysfunction is an important hallmark of amyotrophic lateral sclerosis (ALS). Elevation of creatine kinase (CK) has been reported in 23–75% of ALS patients, but the underlying mechanisms remain unknown. This work aims to enlighten the role of CK as a prognostic factor of respiratory dysfunction in ALS. A retrospective analysis of demographic and clinical variables, CK, functional decline per month (ΔFS), forced vital capacity (%FVC), and mean amplitude of the phrenic nerve compound motor action potential (pCMAP) in 319 ALS patients was conducted. These measurements were evaluated at study entry, and patients were followed from the moment of first observation until death or last follow-up visit. High CK values were defined as above the 90th percentile (CK ≥ P90) adjusted to sex. We analyzed survival and time to non-invasive ventilation (NIV) as proxies for respiratory impairment. Linear regression analysis revealed that high CK was associated with male sex (p < 0.001), spinal onset (p = 0.018), and FVC ≥ 80% (p = 0.038). CK was 23.4% higher in spinal-onset ALS patients (p < 0.001). High CK levels were not linked with an increased risk of death (p = 0.334) in Cox multivariate regression analysis. CK ≥ P90 (HR = 1.001, p = 0.038), shorter disease duration (HR = 0.937, p < 0.001), lower pCMAP (HR = 0.082, p < 0.001), and higher ΔFS (HR = 1.968, p < 0.001) were risk factors for respiratory failure. The association between high CK levels and poorer respiratory outcomes could derive from cellular metabolic stress or a specific phenotype associated with faster respiratory decline. Our study suggests that CK measurement at diagnosis should be more extensively investigated as a possible marker of poor respiratory outcome in future studies, including a larger population of patients. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Author

van Rheenen, Wouter, van der Spek, Rick A. A., Bakker, Mark K., van Vugt, Joke J. F. A., Hop, Paul J., Zwamborn, Ramona A. J., de Klein, Niek, Westra, Harm-Jan, Bakker, Olivier B., Deelen, Patrick, Shireby, Gemma, Hannon, Eilis, Moisse, Matthieu, Baird, Denis, Restuadi, Restuadi, Dolzhenko, Egor, Dekker, Annelot M., Gawor, Klara, Westeneng, Henk-Jan, Tazelaar, Gijs H. P., van Eijk, Kristel R., Kooyman, Maarten, Byrne, Ross P., Doherty, Mark, Heverin, Mark, Al Khleifat, Ahmad, Iacoangeli, Alfredo, Shatunov, Aleksey, Ticozzi, Nicola, Cooper-Knock, Johnathan, Smith, Bradley N., Gromicho, Marta, Chandran, Siddharthan, Pal, Suvankar, Morrison, Karen E., Shaw, Pamela J., Hardy, John, Orrell, Richard W., Sendtner, Michael, Meyer, Thomas, Başak, Nazli, van der Kooi, Anneke J., Ratti, Antonia, Fogh, Isabella, Gellera, Cinzia, Lauria, Giuseppe, Corti, Stefania, Cereda, Cristina, Sproviero, Daisy, D’Alfonso, Sandra, Sorarù, Gianni, Siciliano, Gabriele, Filosto, Massimiliano, Padovani, Alessandro, Chiò, Adriano, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Canosa, Antonio, Grassano, Maurizio, Beghi, Ettore, Pupillo, Elisabetta, Logroscino, Giancarlo, Nefussy, Beatrice, Osmanovic, Alma, Nordin, Angelica, Lerner, Yossef, Zabari, Michal, Gotkine, Marc, Baloh, Robert H., Bell, Shaughn, Vourc’h, Patrick, Corcia, Philippe, Couratier, Philippe, Millecamps, Stéphanie, Meininger, Vincent, Salachas, François, Mora Pardina, Jesus S., Assialioui, Abdelilah, Rojas-García, Ricardo, Dion, Patrick A., Ross, Jay P., Ludolph, Albert C., Weishaupt, Jochen H., Brenner, David, Freischmidt, Axel, Bensimon, Gilbert, Brice, Alexis, Durr, Alexandra, Payan, Christine A. M., Saker-Delye, Safa, Wood, Nicholas W., Topp, Simon, Rademakers, Rosa, Tittmann, Lukas, Lieb, Wolfgang, Franke, Andre, Ripke, Stephan, Braun, Alice, Kraft, Julia, Whiteman, David C., Olsen, Catherine M., Uitterlinden, Andre G., Hofman, Albert, Rietschel, Marcella, Cichon, Sven, Nöthen, Markus M., Amouyel, Philippe, Traynor, Bryan J., Singleton, Andrew B., Mitne Neto, Miguel, Cauchi, Ruben J., Ophoff, Roel A., Wiedau-Pazos, Martina, Lomen-Hoerth, Catherine, van Deerlin, Vivianna M., Grosskreutz, Julian, Roediger, Annekathrin, Gaur, Nayana, Jörk, Alexander, Barthel, Tabea, Theele, Erik, Ilse, Benjamin, Stubendorff, Beatrice, Witte, Otto W., Steinbach, Robert, Hübner, Christian A., Graff, Caroline, Brylev, Lev, Fominykh, Vera, Demeshonok, Vera, Ataulina, Anastasia, Rogelj, Boris, Koritnik, Blaž, Zidar, Janez, Ravnik-Glavač, Metka, Glavač, Damjan, Stević, Zorica, Drory, Vivian, Povedano, Monica, Blair, Ian P., Kiernan, Matthew C., Benyamin, Beben, Henderson, Robert D., Furlong, Sarah, Mathers, Susan, McCombe, Pamela A., Needham, Merrilee, Ngo, Shyuan T., Nicholson, Garth A., Pamphlett, Roger, Rowe, Dominic B., Steyn, Frederik J., Williams, Kelly L., Mather, Karen A., Sachdev, Perminder S., Henders, Anjali K., Wallace, Leanne, de Carvalho, Mamede, Pinto, Susana, Petri, Susanne, Weber, Markus, Rouleau, Guy A., Silani, Vincenzo, Curtis, Charles J., Breen, Gerome, Glass, Jonathan D., Brown, Jr., Robert H., Landers, John E., Shaw, Christopher E., Andersen, Peter M., Groen, Ewout J. N., van Es, Michael A., Pasterkamp, R. Jeroen, Fan, Dongsheng, Garton, Fleur C., McRae, Allan F., Davey Smith, George, Gaunt, Tom R., Eberle, Michael A., Mill, Jonathan, McLaughlin, Russell L., Hardiman, Orla, Kenna, Kevin P., Wray, Naomi R., Tsai, Ellen, Runz, Heiko, Franke, Lude, Al-Chalabi, Ammar, Van Damme, Philip, van den Berg, Leonard H., and Veldink, Jan H.

Published
2021
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38. European Academy of Neurology (EAN) guideline on the management of amyotrophic lateral sclerosis in collaboration with European Reference Network for Neuromuscular Diseases (ERNEURO‐NMD)

Author

Van Damme, Philip, primary, Al‐Chalabi, Ammar, additional, Andersen, Peter M., additional, Chiò, Adriano, additional, Couratier, Philippe, additional, De Carvalho, Mamede, additional, Hardiman, Orla, additional, Kuźma‐Kozakiewicz, Magdalena, additional, Ludolph, Albert, additional, McDermott, Christopher J., additional, Mora, Jesus S., additional, Petri, Susanne, additional, Probyn, Katrin, additional, Reviers, Evy, additional, Salachas, François, additional, Silani, Vincenzo, additional, Tysnes, Ole‐Bjørn, additional, van den Berg, Leonard H., additional, Villanueva, Gemma, additional, and Weber, Markus, additional

Published
2024
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40. The Incidence of Guillain-Barré Syndrome during COVID-19 Pandemic: A Portuguese Multicentric Retrospective Study

Author

Vítor, Joana, primary, Moniz Dionísio, Joana, additional, Campos, Catarina, additional, Oliveira Santos, Miguel, additional, Cruz, Simão, additional, Castelo, Juliana, additional, Castro, José, additional, Castro, Isabel, additional, Santos, Mariana, additional, Vale, José, additional, De Carvalho, Mamede, additional, and Conceição, Isabel, additional

Published
2024
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43. Non-invasive Spinal Cord Stimulation: Relevance of Modelling Studies in Clinical Protocol Design

Author

Fernandes, Sofia Rita, Pereira, Mariana, de Carvalho, Mamede, Miranda, Pedro Cavaleiro, Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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44. Patient Stratification Using Clinical and Patient Profiles: Targeting Personalized Prognostic Prediction in ALS

Author

Pires, Sofia, Gromicho, Marta, Pinto, Susana, de Carvalho, Mamede, Madeira, Sara C., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2020
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45. Unravelling Disease Presentation Patterns in ALS Using Biclustering for Discriminative Meta-Features Discovery

Author

Matos, Joana, Pires, Sofia, Aidos, Helena, Gromicho, Marta, Pinto, Susana, de Carvalho, Mamede, Madeira, Sara C., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2020
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