Publications

Liu, Mengmeng, Gopal Srivastava, J. Ramanujam, and Michal Brylinski. “Augmented Drug Combination Dataset to Improve the Performance of Machine Learning Models Predicting Synergistic Anticancer Effects.” Scientific Reports 14, no. 1 (January 18, 2024): 1668. https://doi.org/10.1038/s41598-024-51940-9.

Modonutti, Daniele, Sami E. Majdalany, Mohit Butaney, Matthew J. Davis, Nicholas Corsi, Akshay Sood, Quoc‐Dien Trinh, et al. “Conditional Survival Does Not Improve over Time in Metastatic Castration‐resistant Prostate Cancer Patients Undergoing Docetaxel.” The Prostate, June 8, 2023, pros.24583. https://doi.org/10.1002/pros.24583.

Rafi, Abdul Muntakim, Dmitry Penzar, Daria Nogina, Dohoon Lee, Eeshit Dhaval Vaishnav, Danyeong Lee, Nayeon Kim, et al. “Evaluation and Optimization of Sequence-Based Gene Regulatory Deep Learning Models.” Preprint. Genomics, April 28, 2023. https://doi.org/10.1101/2023.04.26.538471.

Sieberts, Solveig K., Henryk Borzymowski, Yuanfang Guan, Yidi Huang, Ayala Matzner, Alex Page, Izhar Bar-Gad, et al. “Developing Better Digital Health Measures of Parkinson’s Disease Using Free Living Data and a Crowdsourced Data Analysis Challenge.” Edited by Crina Grosan. PLOS Digital Health 2, no. 3 (March 28, 2023): e0000208. https://doi.org/10.1371/journal.pdig.0000208.

Golob, Jonathan L., Tomiko T. Oskotsky, Alice S. Tang, Alennie Roldan, Verena Chung, Connie W.Y. Ha, Ronald J. Wong, et al. “Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.” Preprint. Obstetrics and Gynecology, March 9, 2023. https://doi.org/10.1101/2023.03.07.23286920.

Lei, Jimeng, Zongheng Cai, Xinyi He, Wanting Zheng, and Jianxiao Liu. “An Approach of Gene Regulatory Network Construction Using Mixed Entropy Optimizing Context-Related Likelihood Mutual Information.” Edited by Valentina Boeva. Bioinformatics 39, no. 1 (January 1, 2023): btac717. https://doi.org/10.1093/bioinformatics/btac717.

Banerjee, Jineta, Jaclyn N. Taroni, Robert J. Allaway, Deepashree Venkatesh Prasad, Justin Guinney, and Casey Greene. “Machine Learning in Rare Disease.” Nature Methods 20, no. 6 (2023): 803–14. https://doi.org/10.1038/s41592-023-01886-z.

Segura-Ortiz, Adrián, José García-Nieto, José F. Aldana-Montes, and Ismael Navas-Delgado. “GENECI: A Novel Evolutionary Machine Learning Consensus-Based Approach for the Inference of Gene Regulatory Networks.” Computers in Biology and Medicine 155 (2023): 106653. https://doi.org/10.1016/j.compbiomed.2023.106653.

Işık, Yunus Emre, and Zafer Aydın. “Comparative Analysis of Machine Learning Approaches for Predicting Respiratory Virus Infection and Symptom Severity.” PeerJ 11 (2023): e15552. https://doi.org/10.7717/peerj.15552.

Mason, Mike, Óscar Lapuente-Santana, Anni S. Halkola, Wenyu Wang, Raghvendra Mall, Xu Xiao, Jacob Kaufman, et al. “A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer.” Preprint. Cancer Biology, December 8, 2022. https://doi.org/10.1101/2022.12.05.518667.

Sun, Dongmei, Thanh M. Nguyen, Robert J. Allaway, Jelai Wang, Verena Chung, Thomas V. Yu, Michael Mason, et al. “A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.” JAMA Network Open 5, no. 8 (August 29, 2022): e2227423. https://doi.org/10.1001/jamanetworkopen.2022.27423.

White, Brian S., Aurélien De Reyniès, Aaron M. Newman, Joshua J. Waterfall, Andrew Lamb, Florent Petitprez, Alberto Valdeolivas, et al. “Community Assessment of Methods to Deconvolve Cellular Composition from Bulk Gene Expression.” Preprint. Bioinformatics, June 5, 2022. https://doi.org/10.1101/2022.06.03.494221.

Yan, Yan, Feng Jiang, Xinan Zhang, and Tianhai Tian. “Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.” Entropy 24, no. 5 (May 13, 2022): 693. https://doi.org/10.3390/e24050693.

Jiang, Xiaohan, and Xiujun Zhang. “RSNET: Inferring Gene Regulatory Networks by a Redundancy Silencing and Network Enhancement Technique.” BMC Bioinformatics 23, no. 1 (May 6, 2022): 165. https://doi.org/10.1186/s12859-022-04696-w.

Cazares, Tareian A., Faiz W. Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Anthony T. Bejjani, Joseph A. Wayman, et al. “MaxATAC: Genome-Scale Transcription-Factor Binding Prediction from ATAC-Seq with Deep Neural Networks.” Preprint. Bioinformatics, January 29, 2022. https://doi.org/10.1101/2022.01.28.478235.

Alexander, Roger P., Robert R Kitchen, Juan Pablo Tosar, Matthew Roth, Pieter Mestdagh, Klaas E. A. Max, Joel Rozowsky, et al. “Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.” Frontiers in Genetics 12 (January 3, 2022): 778416. https://doi.org/10.3389/fgene.2021.778416.

Gong, Wuming, Hyunwoo J. Kim, Daniel J. Garry, and Il-Youp Kwak. “Single Cell Lineage Reconstruction Using Distance-Based Algorithms and the R Package, DCLEAR.” BMC Bioinformatics 23, no. 1 (2022): 103. https://doi.org/10.1186/s12859-022-04633-x.

Gill, Jaskaran, Madhu Chetty, Adrian Shatte, and Jennifer Hallinan. “Combining Kinetic Orders for Efficient S-System Modelling of Gene Regulatory Network.” Biosystems 220 (2022): 104736. https://doi.org/10.1016/j.biosystems.2022.104736.

Douglass, Eugene F., Robert J. Allaway, Bence Szalai, Wenyu Wang, Tingzhong Tian, Adrià Fernández-Torras, Ron Realubit, et al. “A Community Challenge for a Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data.” Cell Reports Medicine 3, no. 1 (2022): 100492. https://doi.org/10.1016/j.xcrm.2021.100492.

Shang, Junliang, Jing Wang, Yan Sun, Feng Li, Jin-Xing Liu, and Honghai Zhang. “Multiscale Part Mutual Information for Quantifying Nonlinear Direct Associations in Networks.” Edited by Jonathan Wren. Bioinformatics 37, no. 18 (September 29, 2021): 2920–29. https://doi.org/10.1093/bioinformatics/btab182.

Xiong, Zhaoping, Minji Jeon, Robert J. Allaway, Jaewoo Kang, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, et al. “Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: The Multi-Targeting Drug DREAM Challenge.” Edited by Edwin Wang. PLOS Computational Biology 17, no. 9 (September 14, 2021): e1009302. https://doi.org/10.1371/journal.pcbi.1009302.

Cichońska, Anna, Balaguru Ravikumar, Robert J. Allaway, Fangping Wan, Sungjoon Park, Olexandr Isayev, Shuya Li, et al. “Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors.” Nature Communications 12, no. 1 (June 3, 2021): 3307. https://doi.org/10.1038/s41467-021-23165-1.

Sieberts, Solveig K., Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, et al. “Crowdsourcing Digital Health Measures to Predict Parkinson’s Disease Severity: The Parkinson’s Disease Digital Biomarker DREAM Challenge.” Npj Digital Medicine 4, no. 1 (March 19, 2021): 53. https://doi.org/10.1038/s41746-021-00414-7.

Coquet, Jean, Nicolas Bievre, Vincent Billaut, Martin Seneviratne, Christopher J. Magnani, Selen Bozkurt, James D. Brooks, and Tina Hernandez-Boussard. “Assessment of a Clinical Trial–Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer.” JAMA Network Open 4, no. 1 (January 22, 2021): e2031730. https://doi.org/10.1001/jamanetworkopen.2020.31730.

Bergquist, Timothy, Thomas Schaffter, Yao Yan, Thomas Yu, Justin Prosser, Jifan Gao, Guanhua Chen, et al. “Evaluation of Crowdsourced Mortality Prediction Models as a Framework for Assessing AI in Medicine.” Preprint. Health Informatics, January 20, 2021. https://doi.org/10.1101/2021.01.18.21250072.

Chen, Chen, Jie Hou, Xiaowen Shi, Hua Yang, James A. Birchler, and Jianlin Cheng. “DeepGRN: Prediction of Transcription Factor Binding Site across Cell-Types Using Attention-Based Deep Neural Networks.” BMC Bioinformatics 22, no. 1 (2021): 38. https://doi.org/10.1186/s12859-020-03952-1.

Vincent, Benjamin G., Joseph D. Szustakowski, Parul Doshi, Michael Mason, Justin Guinney, and David P. Carbone. “Pursuing Better Biomarkers for Immunotherapy Response in Cancer Through a Crowdsourced Data Challenge.” JCO Precision Oncology, no. 5 (2021): 51–54. https://doi.org/10.1200/PO.20.00371.

Gabor, Attila, Marco Tognetti, Alice Driessen, Jovan Tanevski, Baosen Guo, Wencai Cao, He Shen, et al. “Cell‐to‐cell and Type‐to‐type Heterogeneity of Signaling Networks: Insights from the Crowd.” Molecular Systems Biology 17, no. 10 (2021): e10402. https://doi.org/10.15252/msb.202110402.

Gong, Wuming, Alejandro A. Granados, Jingyuan Hu, Matthew G. Jones, Ofir Raz, Irepan Salvador-Martínez, Hanrui Zhang, et al. “Benchmarked Approaches for Reconstruction of in Vitro Cell Lineages and in Silico Models of C. Elegans and M. Musculus Developmental Trees.” Cell Systems 12, no. 8 (2021): 810-826.e4. https://doi.org/10.1016/j.cels.2021.05.008.

Creason, Allison, David Haan, Kristen Dang, Kami E. Chiotti, Matthew Inkman, Andrew Lamb, Thomas Yu, et al. “A Community Challenge to Evaluate RNA-Seq, Fusion Detection, and Isoform Quantification Methods for Cancer Discovery.” Cell Systems 12, no. 8 (2021): 827-838.e5. https://doi.org/10.1016/j.cels.2021.05.021.

Meyer, Pablo, and Julio Saez-Rodriguez. “Advances in Systems Biology Modeling: 10 Years of Crowdsourcing DREAM Challenges.” Cell Systems 12, no. 6 (2021): 636–53. https://doi.org/10.1016/j.cels.2021.05.015.

Tarca, Adi L., Bálint Ármin Pataki, Roberto Romero, Marina Sirota, Yuanfang Guan, Rintu Kutum, Nardhy Gomez-Lopez, et al. “Crowdsourcing Assessment of Maternal Blood Multi-Omics for Predicting Gestational Age and Preterm Birth.” Cell Reports Medicine 2, no. 6 (2021): 100323. https://doi.org/10.1016/j.xcrm.2021.100323.

Roy, Subhrajit, Isabell Kiral, Mahtab Mirmomeni, Todd Mummert, Alan Braz, Jason Tsay, Jianbin Tang, et al. “Evaluation of Artificial Intelligence Systems for Assisting Neurologists with Fast and Accurate Annotations of Scalp Electroencephalography Data.” EBioMedicine 66 (2021): 103275. https://doi.org/10.1016/j.ebiom.2021.103275.

Carpenter, Kristy, Alexander Pilozzi, and Xudong Huang. “A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.” Molecules 25, no. 15 (July 24, 2020): 3372. https://doi.org/10.3390/molecules25153372.

Ma, Weiping, Sunkyu Kim, Shrabanti Chowdhury, Zhi Li, Mi Yang, Seungyeul Yoo, Francesca Petralia, et al. “DreamAI: Algorithm for the Imputation of Proteomics Data.” Preprint. Bioinformatics, July 22, 2020. https://doi.org/10.1101/2020.07.21.214205.

Pham, Vu Vh, Xiaomei Li, Buu Truong, Thin Nguyen, Lin Liu, Jiuyong Li, and Thuc D Le. “The Winning Methods for Predicting Cellular Position in the DREAM Single Cell Transcriptomics Challenge.” Preprint. Systems Biology, May 10, 2020. https://doi.org/10.1101/2020.05.09.086397.

Schaffter, Thomas, Diana S. M. Buist, Christoph I. Lee, Yaroslav Nikulin, Dezso Ribli, Yuanfang Guan, William Lotter, et al. “Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.” JAMA Network Open 3, no. 3 (March 2, 2020): e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265.

Yang, Mi, Francesca Petralia, Zhi Li, Hongyang Li, Weiping Ma, Xiaoyu Song, Sunkyu Kim, et al. “Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.” Cell Systems 11, no. 2 (2020): 186-195.e9. https://doi.org/10.1016/j.cels.2020.06.013.

Multiple Myeloma DREAM Consortium, Mike J. Mason, Carolina Schinke, Christine L. P. Eng, Fadi Towfic, Fred Gruber, Andrew Dervan, et al. “Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 as Marker of Aggressive Disease.” Leukemia 34, no. 7 (2020): 1866–74. https://doi.org/10.1038/s41375-020-0742-z.

DREAM SMC-Het Participants, Adriana Salcedo, Maxime Tarabichi, Shadrielle Melijah G. Espiritu, Amit G. Deshwar, Matei David, Nathan M. Wilson, et al. “A Community Effort to Create Standards for Evaluating Tumor Subclonal Reconstruction.” Nature Biotechnology 38, no. 1 (2020): 97–107. https://doi.org/10.1038/s41587-019-0364-z.

Ford, Colby T., and Daniel Janies. “Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria.” F1000Research 9 (2020): 62. https://doi.org/10.12688/f1000research.21539.5.

Menden, Michael P., Dennis Wang, Mike J. Mason, Bence Szalai, Krishna C. Bulusu, Yuanfang Guan, Thomas Yu, et al. “Community Assessment to Advance Computational Prediction of Cancer Drug Combinations in a Pharmacogenomic Screen.” Nature Communications 10, no. 1 (June 17, 2019): 2674. https://doi.org/10.1038/s41467-019-09799-2.

Kueffner, Robert, Neta Zach, Maya Bronfeld, Raquel Norel, Nazem Atassi, Venkat Balagurusamy, Barbara Di Camillo, et al. “Stratification of Amyotrophic Lateral Sclerosis Patients: A Crowdsourcing Approach.” Scientific Reports 9, no. 1 (January 24, 2019): 690. https://doi.org/10.1038/s41598-018-36873-4.

Ellrott, Kyle, Alex Buchanan, Allison Creason, Michael Mason, Thomas Schaffter, Bruce Hoff, James Eddy, et al. “Reproducible Biomedical Benchmarking in the Cloud: Lessons from Crowd-Sourced Data Challenges.” Genome Biology 20, no. 1 (2019): 195. https://doi.org/10.1186/s13059-019-1794-0.

The DREAM Module Identification Challenge Consortium, Sarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, et al. “Assessment of Network Module Identification across Complex Diseases.” Nature Methods 16, no. 9 (2019): 843–52. https://doi.org/10.1038/s41592-019-0509-5.

Razzaq, Misbah, Loïc Paulevé, Anne Siegel, Julio Saez-Rodriguez, Jérémie Bourdon, and Carito Guziolowski. “Computational Discovery of Dynamic Cell Line Specific Boolean Networks from Multiplex Time-Course Data.” Edited by Joerg Stelling. PLOS Computational Biology 14, no. 10 (October 29, 2018): e1006538. https://doi.org/10.1371/journal.pcbi.1006538.

Sendorek, Dorota H., Cristian Caloian, Kyle Ellrott, J. Christopher Bare, Takafumi N. Yamaguchi, Adam D. Ewing, Kathleen E. Houlahan, et al. “Germline Contamination and Leakage in Whole Genome Somatic Single Nucleotide Variant Detection.” BMC Bioinformatics 19, no. 1 (2018): 28. https://doi.org/10.1186/s12859-018-2046-0.

ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants, Anna Y. Lee, Adam D. Ewing, Kyle Ellrott, Yin Hu, Kathleen E. Houlahan, J. Christopher Bare, et al. “Combining Accurate Tumor Genome Simulation with Crowdsourcing to Benchmark Somatic Structural Variant Detection.” Genome Biology 19, no. 1 (2018): 188. https://doi.org/10.1186/s13059-018-1539-5.

Laajala, Teemu D., Justin Guinney, and James C. Costello. “Community Mining of Open Clinical Trial Data.” Oncotarget 8, no. 47 (October 10, 2017): 81721–22. https://doi.org/10.18632/oncotarget.20853.

Bakas, Spyridon, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S. Kirby, John B. Freymann, Keyvan Farahani, and Christos Davatzikos. “Advancing The Cancer Genome Atlas Glioma MRI Collections with Expert Segmentation Labels and Radiomic Features.” Scientific Data 4, no. 1 (September 5, 2017): 170117. https://doi.org/10.1038/sdata.2017.117.