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1. Analyzing Norm Violations in Live-Stream Chat

Author

Moon, Jihyung, Lee, Dong-Ho, Cho, Hyundong, Jin, Woojeong, Park, Chan Young, Kim, Minwoo, May, Jonathan, Pujara, Jay, and Park, Sungjoon

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%., Comment: 17 pages, 8 figures, 15 tables

Published
2023
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3. Unsupervised learning of topological phase diagram using topological data analysis

Author

Park, Sungjoon, Hwang, Yoonseok, and Yang, Bohm-Jung

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Data Analysis, Statistics and Probability, Mesoscale and Nanoscale Physics (cond-mat.mes-hall), FOS: Physical sciences, Computational Physics (physics.comp-ph), Physics - Computational Physics, Data Analysis, Statistics and Probability (physics.data-an)
Abstract

Topology and machine learning are two actively researched topics not only in condensed matter physics, but also in data science. Here, we propose the use of topological data analysis in unsupervised learning of the topological phase diagrams. This is possible because the quantum distance can capture the shape of the space formed by the Bloch wavefunctions as we sweep over the Brillouin zone. Therefore, if we minimize the volume of the space formed by the wavefunction through a continuous deformation, the wavefunctions will end up forming distinct spaces which depend on the topology of the wavefunctions. Combining this observation with the topological data analysis, which provides tools such as the persistence diagram to capture the topology of the space formed by the wavefunctions, we can cluster together Hamiltonians that give rise to similar persistence diagrams after the deformation. By examining these clusters as well as representative persistence diagrams in the clusters, we can draw the phase diagram as well as distinguish between topologically trivial and nontrivial phases. Our proposal to minimize the volume can be interpreted as finding geodesics in 1D Brillouin zone, and minimal surfaces in 2D and higher-dimensional Brillouin zones. Using this interpretation, we can guarantee the convergence of the minimization under certain conditions, which is an outstanding feature of our algorithm. We demonstrate the working principles of our machine learning algorithm using various models., 10 pages, 9 figures. Published version

Published
2022
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4. Additional file 1 of Optimal cardiopulmonary resuscitation duration for favorable neurological outcomes after out-of-hospital cardiac arrest

Author

Park, SungJoon, Lee, Sung Woo, Han, Kap Su, Lee, Eui Jung, Jang, Dong-Hyun, Lee, Si Jin, Lee, Ji Sung, and Kim, Su Jin

Abstract

Additional file 1: Table S1. Probability of favorable neurological outcome (A) and cumulative proportion (B) of favorable neurological outcome at discharge by total CPR duration stratified by initial shockable rhythm and witnessed arrest

Published
2022
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5. Theory of transverse magnetization in spin-orbit coupled antiferromagnets

Author

Oh, Taekoo, Park, Sungjoon, and Yang, Bohm-Jung

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Mesoscale and Nanoscale Physics, Strongly Correlated Electrons (cond-mat.str-el), Mesoscale and Nanoscale Physics (cond-mat.mes-hall), FOS: Physical sciences
Abstract

Some antiferromagnets under a magnetic field develop magnetization perpendicular to the field as well as more conventional ones parallel to the field. So far, the transverse magnetization (TM) has been attributed to either spin canting effect or the presence of cluster magnetic multipolar ordering. However, a general theory of TM based on microscopic understanding is still missing. Here, we construct a general microscopic theory of TM in antiferromagnets with cluster magnetic multipolar ordering by considering classical spin Hamiltonians with spin anisotropy that arises from the spin-orbit coupling. First, from general symmetry analysis, we show that TM can appear only when all crystalline symmetries are broken other than the antiunitary mirror, antiunitary two-fold rotation, and inversion symmetries. Moreover, by analyzing spin Hamiltonians, we show that TM always appears when the degenerate ground state manifold of the spin Hamiltonian is discrete. On the other hand, when the degenerate ground state manifold is continuous, TM generally does not appear except when the magnetic field direction and the spin configuration satisfy specific geometric conditions under single-ion anisotropy. Finally, we show that TM can induce anomalous planar Hall Effect, a unique transport phenomenon that can be used to probe multipolar antiferromagnetic structures. We believe that our theory provides a useful guideline for understanding the anomalous magnetic responses of the antiferromagnets with complex magnetic structures., Comment: 4 Figures, 8 Supplementary Figures, 3 Tables, 23 Pages

Published
2022
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6. KOLD: Korean Offensive Language Dataset

Author

Jeong, Younghoon, Oh, Juhyun, Ahn, Jaimeen, Lee, Jongwon, Moon, Jihyung, Park, Sungjoon, and Oh, Alice

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction. These improvements are focused on English and do not transfer well to other languages because of cultural and linguistic differences. In this paper, we present the Korean Offensive Language Dataset (KOLD) comprising 40,429 comments, which are annotated hierarchically with the type and the target of offensive language, accompanied by annotations of the corresponding text spans. We collect the comments from NAVER news and YouTube platform and provide the titles of the articles and videos as the context information for the annotation process. We use these annotated comments as training data for Korean BERT and RoBERTa models and find that they are effective at offensiveness detection, target classification, and target span detection while having room for improvement for target group classification and offensive span detection. We discover that the target group distribution differs drastically from the existing English datasets, and observe that providing the context information improves the model performance in offensiveness detection (+0.3), target classification (+1.5), and target group classification (+13.1). We publicly release the dataset and baseline models., Comment: 9 pages, 2 figures

Published
2022
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8. Perspective taking and reference frames for spatial and social cognition

Author

Park, Sungjoon, Watanabe, Brandon K, and Burte, Heather

Subjects
Theory of Mind, Psychology, Problem Solving, Social cognition, Spatial cognition, Computer-based experiment, Representation
Abstract

When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

Published
2022

9. Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles

Author

Kim, Hyunjae, Sung, Mujeen, Yoon, Wonjin, Park, Sungjoon, and Kang, Jaewoo

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL), Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract

This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge. The main feature of this challenge is that the data consists of full-text articles, while current datasets usually consist of only titles and abstracts. To effectively address the problem, we aim to improve tagging consistency and entity coverage using various methods such as majority voting within the same articles for named entity recognition (NER) and a hybrid approach that combines a dictionary and a neural model for normalization. In the experiments on the NLM-Chem dataset, we show that our methods improve models' performance, particularly in terms of recall. Finally, in the official evaluation of the challenge, our system was ranked 1st in NER by significantly outperforming the baseline model and more than 80 submissions from 16 teams., BioCreative VII Challenge Evaluation Workshop

Published
2021

10. Successful resuscitation of refractory ventricular fibrillation with double sequence defibrillation

Author

Park, SungJoon, Kim, Jung-Youn, Cho, Young-Duck, Lee, Eusun, Shim, Bosun, and Yoon, Young-Hoon

Subjects
survival rate, medicine.medical_specialty, Defibrillation, medicine.medical_treatment, CPR/Resuscitation, Case Report, Critical Care and Intensive Care Medicine, Critical Care Nursing, Chest pain, Internal medicine, defibrillators, medicine, Survival rate, medicine.diagnostic_test, business.industry, Mortality rate, Advanced cardiac life support, lcsh:Medical emergencies. Critical care. Intensive care. First aid, lcsh:RC86-88.9, Emergency department, ventricular fibrillation, medicine.disease, Ventricular fibrillation, Cardiology, advanced cardiac life support, medicine.symptom, business, Electrocardiography
Abstract

In cardiac arrest, if the initial rhythm is ventricular fibrillation (VF) or pulseless ventricular tachycardia, the survival rates are high and good neurologic outcomes are expected. However, the mortality rate increases when refractory ventricular fibrillation (RVF) occurs. We report a case of RVF that was successfully resuscitated with double sequence defibrillation (DSD). A 51-year-old man visited the emergency department with chest pain. The initial electrocardiography showed markedly elevated ST-segment on V1–V5 leads, and VF arrest occurred. Although 10 defibrillations were administered over 20 minutes, there was no response. Two rounds of DSD were performed by placing additional pads on the patient’s anterior-posterior areas and sequentially applying the maximum energy setting. The patient returned to spontaneous circulation and was discharged with cerebral performance category 1 after 14 days of hospital admission. Therefore, DSD could be an option for treatment and termination of RVF.

Published
2021
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12. KLUE: Korean Language Understanding Evaluation

Author

Park, Sungjoon, Moon, Jihyung, Kim, Sungdong, Cho, Won Ik, Han, Jiyoon, Park, Jangwon, Song, Chisung, Kim, Junseong, Song, Yongsook, Oh, Taehwan, Lee, Joohong, Oh, Juhyun, Lyu, Sungwon, Jeong, Younghoon, Lee, Inkwon, Seo, Sangwoo, Lee, Dongjun, Kim, Hyunwoo, Lee, Myeonghwa, Jang, Seongbo, Do, Seungwon, Kim, Sunkyoung, Lim, Kyungtae, Lee, Jongwon, Park, Kyumin, Shin, Jamin, Kim, Seonghyun, Park, Lucy, Oh, Alice, Ha, Jung-Woo, and Cho, Kyunghyun

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. We build all of the tasks from scratch from diverse source corpora while respecting copyrights, to ensure accessibility for anyone without any restrictions. With ethical considerations in mind, we carefully design annotation protocols. Along with the benchmark tasks and data, we provide suitable evaluation metrics and fine-tuning recipes for pretrained language models for each task. We furthermore release the pretrained language models (PLM), KLUE-BERT and KLUE-RoBERTa, to help reproducing baseline models on KLUE and thereby facilitate future research. We make a few interesting observations from the preliminary experiments using the proposed KLUE benchmark suite, already demonstrating the usefulness of this new benchmark suite. First, we find KLUE-RoBERTa-large outperforms other baselines, including multilingual PLMs and existing open-source Korean PLMs. Second, we see minimal degradation in performance even when we replace personally identifiable information from the pretraining corpus, suggesting that privacy and NLU capability are not at odds with each other. Lastly, we find that using BPE tokenization in combination with morpheme-level pre-tokenization is effective in tasks involving morpheme-level tagging, detection and generation. In addition to accelerating Korean NLP research, our comprehensive documentation on creating KLUE will facilitate creating similar resources for other languages in the future. KLUE is available at https://klue-benchmark.com., 76 pages, 10 figures, 36 tables

Published
2021

13. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichońska, Anna, Ravikumar, Balaguru, Allaway, Robert J., Wan, Fangping, Park, Sungjoon, Isayev, Olexandr, Li, Shuya, Mason, Michael, Lamb, Andrew, Tanoli, Ziaurrehman, Jeon, Minji, Kim, Sunkyu, Popova, Mariya, Capuzzi, Stephen, Zeng, Jianyang, Dang, Kristen, Koytiger, Gregory, Kang, Jaewoo, Wells, Carrow I., Willson, Timothy M., Oršolić, Davor, Lučić, Bono, Stepanić, Višnja, Šmuc, Tomislav, Oprea, Tudor I., Schlessinger, Avner, Drewry, David H., Stolovitzky, Gustavo, Wennerberg, Krister, Guinney, Justin, and Aittokallio, Tero

Subjects
Biology
Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Published
2021

14. Pointing North Online: Using photographs of known environments to evaluate north pointing accuracy

Author

Deshpande, Tanvi, Park, Sungjoon, and Burte, Heather

Subjects
cognitive science
Abstract

Previous research has found that cognitive maps are not consistently oriented towards north as people tend to bias their north-pointing estimates towards nearby roads (Brunyé et al., 2015). While pointing studies are typically conducted within familiar environments, it is not clear whether north-pointing estimates will show a similar bias towards nearby roads when individuals are not physically located in the environment. In essence, a north-pointing task when not located within the environment is a perspective-taking task. In a series of experiments, participants rated their familiarity with the Texas A&M campus and two nearby cities, completed a self-assessment of sense-of-direction, and then pointed towards north. The pointing task used photographs of the A&M campus to provide a location and initial orientation. These experiments provide new insights into individual differences in north pointing and perspective-taking skills when an individual is not physically present within the environment.

Published
2021

15. Magnetoelastic coupling enabled tunability of magnon spin current generation in 2D antiferromagnets

Author

Bazazzadeh, Nasim, Hamdi, Mohammad, Park, Sungjoon, Khavasi, Amin, Mohseni, S. Majid, and Sadeghi, Ali

Subjects
Condensed Matter - Materials Science, Condensed Matter::Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Mesoscale and Nanoscale Physics (cond-mat.mes-hall), Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Condensed Matter::Strongly Correlated Electrons
Abstract

We theoretically investigate the magnetoelastic coupling (MEC) and its effect on magnon transport in two-dimensional antiferromagnets with a honeycomb lattice. MEC coeffcient along with magnetic exchange parameters and spring constants are computed for monolayers of transition metal trichalcogenides with N\'eel order ($\text{MnPS}_3$ and $\text{VPS}_3$) and zigzag order ($\text{CrSiTe}_3$, $\text{NiPS}_3$ and $\text{NiPSe}_3$) by $ab$ $initio$ calculations. Using these parameters, we predict that the spin-Nernst coefficient is significantly enhanced due to magnetoelastic coupling. Our study shows that although Dzyaloshinskii-Moriya interaction can produce spin Nernst effect in these materials, other mechanisms such as magnon-phonon coupling should be taken into account. We also demonstrate that the magnetic anisotropy is an important factor for control of magnon-phonon hybridization and enhancement of the Berry curvature and thus the spin-Nernst coefficient. Our results pave the way towards gate tunable spin current generation in 2D magnets by SNE via electric field modulation of MEC and anisotropy.

Published
2021
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16. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichońska, Anna, Ravikumar, Balaguru, Allaway, Robert J., Wan, Fangping, Park, Sungjoon, Isayev, Olexandr, Li, Shuya, Mason, Michael, Lamb, Andrew, Tanoli, Ziaurrehman, Jeon, Minji, Kim, Sunkyu, Popova, Mariya, Capuzzi, Stephen, Zeng, Jianyang, Dang, Kristen, Koytiger, Gregory, Kang, Jaewoo, Wells, Carrow I., Willson, Timothy M., The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, Oršolić, Davor, Lučić, Bono, Stepanić, Višnja, Šmuc, Tomislav, Oprea, Tudor I., Schlessinger, Avner, Drewry, David H., Stolovitzky, Gustavo, Wennerberg, Krister, Guinney, Justin, and Aittokallio, Tero

Subjects
PREDICTION, DISCOVERY, Medicine and Health Sciences, Cheminformatics, Kinases, Machine learning, DRUG, PHARMACOLOGY, PACKAGE
Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.

Published
2021

18. Crowdsourced mapping of unexplored target space of kinase inhibitors

Author

Cichonska, Anna, Ravikumar, Balaguru, Allaway, Robert J., Wan, Fangping, Park, Sungjoon, Isayev, Olexandr, Li, Shuya, Mason, Michael, Lamb, Andrew, Tanoli, Ziaurrehman, Jeon, Minji, Kim, Sunkyu, Popova, Mariya, Capuzzi, Stephen, Zeng, Jianyang, Dang, Kristen, Koytiger, Gregory, Kang, Jaewoo, Wells, Carrow I., Willson, Timothy M., Oprea, Tudor I, Schlessinger, Avner, Drewry, David H., Stolovitzky, Gustavo, Wennerberg, Krister, Guinney, Justin, and Aittokallio, Tero Antero

Subjects
Proteomics, Databases, Pharmaceutical, Cheminformatics, Drug Evaluation, Preclinical, Kinases, Models, Biological, Article, Machine Learning, Benchmarking, Kinetics, Deep Learning, Models, Chemical, Drug Discovery, Crowdsourcing, Humans, Regression Analysis, Protein Kinase Inhibitors, Protein Kinases, Algorithms
Abstract

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome., The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.

Published
2020

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

Author

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 A., 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. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, Gonen, Mehmet, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong T., De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. 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, and Computer Science

Subjects
health care economics and organizations
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. AstraZeneca European Union Horizon 2020 research [668858 PrECISE] Joint Research Center for Computational Biomedicine (Bayer AG) National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences Wellcome Trust [102696, 206194] 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).

Published
2019

20. Assessment of network module identification across complex diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Natoli, Ted, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Marbach, Daniel, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Mercer, Johnathan, Micarelli, Elisa, Monaco, Alfonso, Narayan, Rajiv, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., DREAM Module Identification Challenge Consortium, Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Sáez Rodríguez, Julio, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Subramanian, Aravind, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Slonim, Donna K., Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Zhang, Jitao D., Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Stolovitzky, Gustavo, Vivek, Jayaswal, von Mering, Christian, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Kutalik, Zoltán, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Lage, Kasper, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, Zuccon, Guido, Crawford, Jake, Cowen, Lenore J., Bergmann, Sven, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Tomasoni, Mattia, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Fang, Tao, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, De Domenico, Manlio, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Lamparter, David, Del Sol, Antonio, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Lin, Junyuan, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hescott, Benjamin, Hu, Xiaozhe, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan T., Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Le, Thuc Duy, Li, Jiuyong, Liu, Lin, Zhang, W, Marbach, D, The DREAM Module Identification Challenge Consortium, Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Spinelli, Lionel, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Theories and Approaches of Genomic Complexity (TAGC), DREAM Module Identification Challenge Consortium, Aicheler, F., Amoroso, N., Arenas, A., Azhagesan, K., Baker, A., Banf, M., Batzoglou, S., Baudot, A., Bellotti, R., Bergmann, S., Boroevich, K.A., Brun, C., Cai, S., Caldera, M., Calderone, A., Cesareni, G., Chen, W., Chichester, C., Choobdar, S., Cowen, L., Crawford, J., Cui, H., Dao, P., De Domenico, M., Dhroso, A., Didier, G., Divine, M., Del Sol, A., Fang, T., Feng, X., Flores-Canales, J.C., Fortunato, S., Gitter, A., Gorska, A., Guan, Y., Guénoche, A., Gómez, S., Hamza, H., Hartmann, A., He, S., Heijs, A., Heinrich, J., Hescott, B., Hu, X., Hu, Y., Huang, X., Hughitt, V.K., Jeon, M., Jeub, L., Johnson, N.T., Joo, K., Joung, I., Jung, S., Kalko, S.G., Kamola, P.J., Kang, J., Kaveelerdpotjana, B., Kim, M., Kim, Y.A., Kohlbacher, O., Korkin, D., Krzysztof, K., Kunji, K., Kutalik, Z., Lage, K., Lamparter, D., Lang-Brown, S., Le, T.D., Lee, J., Lee, S., Li, D., Li, J., Lin, J., Liu, L., Loizou, A., Luo, Z., Lysenko, A., Ma, T., Mall, R., Marbach, D., Mattia, T., Medvedovic, M., Menche, J., Mercer, J., Micarelli, E., Monaco, A., Müller, F., Narayan, R., Narykov, O., Natoli, T., Norman, T., Park, S., Perfetto, L., Perrin, D., Pirrò, S., Przytycka, T.M., Qian, X., Raman, K., Ramazzotti, D., Ramsahai, E., Ravindran, B., Rennert, P., Saez-Rodriguez, J., Schärfe, C., Sharan, R., Shi, N., Shin, W., Shu, H., Sinha, H., Slonim, D.K., Spinelli, L., Srinivasan, S., Subramanian, A., Suver, C., Szklarczyk, D., Tangaro, S., Thiagarajan, S., Tichit, L., Tiede, T., Tripathi, B., Tsherniak, A., Tsunoda, T., Türei, D., Ullah, E., Vahedi, G., Valdeolivas, A., Vivek, J., von Mering, C., Waagmeester, A., Wang, B., Wang, Y., Weir, B.A., White, S., Winkler, S., Xu, K., Xu, T., Yan, C., Yang, L., Yu, K., Yu, X., Zaffaroni, G., Zaslavskiy, M., Zeng, T., Zhang, J.D., Zhang, L., Zhang, W., Zhang, X., Zhang, J., Zhou, X., Zhou, J., Zhu, H., Zhu, J., and Zuccon, G.

Subjects
Identification methods, Cellular signalling networks, Computer science, Population genetics, [SDV]Life Sciences [q-bio], Quantitative Trait Loci, Gene regulatory network, DREAM challenge, network, modules, predictions, Genome-wide association study, Computational biology, Biochemistry, Models, Biological, Polymorphism, Single Nucleotide, Gene regulatory networks, Functional clustering, 03 medical and health sciences, Human disease, Humans, Disease, ddc:610, Protein Interaction Maps, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Network module, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Network topology, Gene Expression Profiling, Computational Biology, Cell Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Gene expression profiling, [SDV] Life Sciences [q-bio], Molecular network, Phenotype, Protein network, Network Module Identification, Analysis, Algorithms, Biotechnology, Genome-Wide Association Study
Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology., In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

Published
2019
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21. Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, Subramanian, Aravind, Zhang, Jitao D., Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K., Saez-Rodriguez, Julio, Cowen, Lenore J., Bergmann, Sven, Marbach, Daniel, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, Domenico, Manlio De, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Sol, Antonio del, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan, Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Micarelli, Elisa, Monaco, Alfonso, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Vivek, Jayaswal, Mering, Christian von, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, and Zuccon, Guido

Subjects
Identification methods, Molecular network, Computer science, Association (object-oriented programming), Key (cryptography), Open community, Genome-wide association study, Identification (biology), Computational biology, Disease
Abstract

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

Published
2018
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22. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

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., Veroli, Giovanni Y. Di, 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., Aznar, Victoria Romeo, 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, Cock, Martine De, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzö, Miklós, Cubuk, Cankut, Curtis, Christina, Daele, Dries Van, 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. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, Gonen, Mehmet, De Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K, Huang, R. Stephanie, Hunyady, László, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Don’t Walk, Oliver Bear, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M, Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, Van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Lepp_aho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, Maeyer, Dries De, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Moerbeke, Marijke Van, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, Niz, Carlos De, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Pujana, Miguel Angel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, De Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-Ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, Troyer, Ewoud De, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gábor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, Van Westen, Gerard J. 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, and Zucknick, Manuela

Subjects
49/98, 45, 49/23, article, 49/39, 631/553, 631/114/1305, 631/114/2415, 631/154/1435/2163, 692/4028/67, 13, 3. Good health, 49
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.

23. Assessment of network module identification across complex diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, Subramanian, Aravind, Zhang, Jitao D., Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K., Sáez Rodríguez, Julio, Cowen, Lenore J., Bergmann, Sven, Marbach, Daniel, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, De Domenico, Manlio, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Del Sol, Antonio, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan T., Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Micarelli, Elisa, Monaco, Alfonso, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Vivek, Jayaswal, Von Mering, Christian, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, and Zuccon, Guido

Subjects
3. Good health
Abstract

Nature methods 16(9), 843-852 (2019). doi:10.1038/s41592-019-0509-5, Published by Nature Publishing Group, London [u.a.]

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

Author

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, AstraZeneca-Sanger Drug Combination DREAM Consortium, 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., Aznar, Victoria Romeo, 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, Cserzö, Miklós, 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. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, Gonen, Mehmet, De Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, László, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Don't Walk, Oliver Bear, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, Van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Lepp Aho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Pujana, Miguel Angel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, De Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-Ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gábor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, Van Westen, Gerard J. 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, and Zucknick, Manuela

Subjects
3. Good health
Abstract

Nat Commun 10(1), 2674 (2019). doi:10.1038/s41467-019-09799-2

25. 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. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. 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[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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