Papers

• Ren B, Patil P, Dominici F, Parmigiani G, Trippa L. (2020). Cross-study learning for generalist and specialist predictions.(arXiv)


• Deng Z, Ding F, Dwork C, Hong R, Parmigiani G, Patil P, Sur P (2020). Representation via representations: Domain generalization via adversarially learned invariant representations. (arXiv)


• Loewinger GC, Patil P, Kishida KK, Parmigiani G (2019+). Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing. (bioRxiv)


• Guan Z, Parmigiani G, Patil P (2019+). Merging versus Ensembling in MultiStudy Machine Learning: Theoretical Insight from Random Effects. (arXiv)(under revision, Biometrika)


• Zhang Y, Patil P, Johnson WE, Parmigiani G (2020). Robustifying genomic classifiers to batch effects via ensemble learning. (In press, Bioinformatics)(bioRxiv)
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• Westerman K, Fernández-Sanlés A, Patil P, Sebastiani P, Jacques P, Starr JM, J. Deary I, Liu Q, Liu S, Elosua R, DeMeo DL (2020). Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics. Journal of the American Heart Association. 9(8):e015299.(PDF)


• Nudel J, Bishara AM, de Geus SW, Patil P, Srinivasan J, Hess DT, Woodson J (2020). Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surgical Endoscopy. 17:1-10(PDF)


• Ramchandran M, Patil P, Parmigiani G (2019). Tree-weighting approaches in constructing cross-study learners. Pac Symp Biocomput. 2020 ; 25:451-462. (PDF)(bioRxiv)


• Patil P, Peng RD, Leek JT. (2019). A visual tool for defining reproducibility and replicability. Nature Human Behavior 3, 650{652. (PDF)


• Patil P, Parmigiani G (2018). Training replicable predictors in multiple studies. Proceedings of the National Academy of Sciences, 115(11), 2578-2583. (PDF)


• Patil P, Peng RD, Leek, JT (2016). What Should Researchers Expect When They Replicate Studies? A Statistical View of Replicability in Psychological Science. Perspectives on Psychological Science, 11(4), 539-544. (PDF)


• Patil P, Colantuoni E, Rosenblum MA, Leek JT (2016). Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments. Contemporary Clinical Trials Communications, 3: 48-54. (PDF)


• Patil P, Leek JT (2015). Discussion of "Visualizing statistical models: Removing the blindfold". Statistical Analysis and Data Mining: The ASA Data Science Journal, 8(4), 240-241. (PDF)


• Patil P, Bachant-Winner PO, Haibe-Kains B, Leek JT (2015). Test set bias affects reproducibility of gene signatures. Bioinformatics, 31(14), 2318-2323. (PDF)


• Hyland PL, Burke LS, Pfeiffer RM, Rotunno M, Sun D, Patil P, Wu X, Tucker MA, Goldstein AM, Yang XR (2014). Constitutional promoter methylation and risk of familial melanoma. Epigenetics, 9(5), 685-692. (PDF)


• Fusaro, VA, Patil P, Chi CL, Contant CF, Tonellato PJ (2013). A Systems Approach to Designing Effective Clinical Trials Using Simulations. Circulation, 127(4), 517-526. (PDF)


• Fusaro VA, Patil P, Gafni E, Wall DP, Tonellato PJ (2011). Biomedical Cloud Computing Using Amazon Web Services. PLoS Computational Biology. 7(8):e1002147. (PDF)


• Wall DP, Kudtarkar P, Fusaro VA, Pivovarov R, Patil P, Tonellato PJ (2010). Cloud computing for comparative genomics. BMC Bioinformatics 11:259. (PDF)