Prasad Patil, PhD
Assistant Professor
Boston University School of Public Health
Dept of Biostatistics

PhD, Johns Hopkins University

Dr. Patil is a former postdoctoral research fellow at the Harvard Chan School of Public Health Department of Biostatistics/Dana-Farber Cancer Institute Department of Biostatistics and Computational Biology with Giovanni Parimigiani.
He completed his PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health with Jeff Leek.
His professional interests include personalized medicine, genomics, prediction, data visualization, and study reproducibility/replicability.
Dr. Patil is currently working on:
Multi-study prediction
Statistical definitions for reproducibility and replicability.
Stable and interpretable prediction methods for gene expression data. The contexts are cancer risk classifcation and survival prediction.
Assessing the additional value a genomic signature can provide beyond standard clinical measurements in a randomized trial setting.
Interactive health visualizations executable in one line from R.
Automated analysis templates with the ability to compare results after parameters have been changed.

Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.

iCite Analysis       Copy PMIDs To Clipboard

  1. Zhang Y, Patil P, Evan Johnson W, Parmigiani G. Robustifying Genomic Classifiers To Batch Effects Via Ensemble Learning. Bioinformatics. 2020 Nov 27.View Related Profiles. PMID: 33245114
  2. Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, Woodson J. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc. 2021 Jan; 35(1):182-191.View Related Profiles. PMID: 31953733
  3. Ramchandran M, Patil P, Parmigiani G. Tree-Weighting for Multi-Study Ensemble Learners. Pac Symp Biocomput. 2020; 25:451-462. PMID: 31797618
  4. Patil P, Peng RD, Leek JT. Publisher Correction: A visual tool for defining reproducibility and replicability. Nat Hum Behav. 2019 Aug; 3(8):886. PMID: 31358976
  5. Patil P, Peng RD, Leek JT. A visual tool for defining reproducibility and replicability. Nat Hum Behav. 2019 07; 3(7):650-652. PMID: 31209370
  6. Patil P, Parmigiani G. Training replicable predictors in multiple studies. Proc Natl Acad Sci U S A. 2018 03 13; 115(11):2578-2583. PMID: 29531060; DOI: 10.1073/pnas.1708283115;
  7. Patil P, Peng RD, Leek JT. What Should Researchers Expect When They Replicate Studies? A Statistical View of Replicability in Psychological Science. Perspect Psychol Sci. 2016 07; 11(4):539-44. PMID: 27474140; DOI: 10.1177/1745691616646366;
  8. Patil P, Colantuoni E, Leek JT, Rosenblum M. Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments. Contemp Clin Trials Commun. 2016 Aug 15; 3:48-54. PMID: 29736456; DOI: 10.1016/j.conctc.2016.03.001;
  9. Patil P, Bachant-Winner PO, Haibe-Kains B, Leek JT. Test set bias affects reproducibility of gene signatures. Bioinformatics. 2015 Jul 15; 31(14):2318-23. PMID: 25788628; DOI: 10.1093/bioinformatics/btv157;
  10. Patil P, Leek JT . Discussion of “Visualizing statistical models: Removing the blindfold”. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2015; 8(4):240-241.
Showing 10 of 15 results. Show More

This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 15 publications over 9 distinct years, with a maximum of 3 publications in 2020


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