Prasad Patil

1.4k total citations
51 papers, 746 citations indexed

About

Prasad Patil is a scholar working on Molecular Biology, General Health Professions and Epidemiology. According to data from OpenAlex, Prasad Patil has authored 51 papers receiving a total of 746 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 6 papers in General Health Professions and 6 papers in Epidemiology. Recurrent topics in Prasad Patil's work include Gene expression and cancer classification (5 papers), Molecular Biology Techniques and Applications (4 papers) and Health disparities and outcomes (4 papers). Prasad Patil is often cited by papers focused on Gene expression and cancer classification (5 papers), Molecular Biology Techniques and Applications (4 papers) and Health disparities and outcomes (4 papers). Prasad Patil collaborates with scholars based in United States, India and United Kingdom. Prasad Patil's co-authors include Jeffrey T. Leek, Roger D. Peng, Peter J. Tonellato, Dennis P. Wall, Benjamin Haibe‐Kains, Vincent A. Fusaro, Erik Gafni, Giovanni Parmigiani, Vincent A. Fusaro and Parul Kudtarkar and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Circulation and SHILAP Revista de lepidopterología.

In The Last Decade

Prasad Patil

42 papers receiving 711 citations

Peers

Prasad Patil

ISM

SPU

Stylianos Serghiou United States

Wolfgang Kuchinke Germany

Alison Callahan United States

Fei Yu United States

Alexis Allot United States

Andrew McMurry United States

Jerry Sheehan United States

Mahsa Shabani Belgium

Ram Chilukuri United States

Adrian Burton Australia

Stylianos Serghiou United States

Prasad Patil

175 ×0.8

100 ×1.3

ISM

101 ×1.4

229 ×4.2

SPU

138 ×2.6

Citations per year, relative to Prasad Patil Prasad Patil (= 1×) peers Stylianos Serghiou

Countries citing papers authored by Prasad Patil

Since Specialization

Citations

This map shows the geographic impact of Prasad Patil's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Prasad Patil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prasad Patil more than expected).

Fields of papers citing papers by Prasad Patil

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prasad Patil. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Prasad Patil. The network helps show where Prasad Patil may publish in the future.

Co-authorship network of co-authors of Prasad Patil

This figure shows the co-authorship network connecting the top 25 collaborators of Prasad Patil. A scholar is included among the top collaborators of Prasad Patil based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Prasad Patil. Prasad Patil is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Patil, Prasad, Jonathan I. Levy, Neelakshi Hudda, et al.. (2025). Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable Machine Learning. Environmental Science & Technology. 59(37). 19942–19952. 1 indexed citations

Wang, Xutao, Jerrold J. Ellner, Natasha S. Hochberg, et al.. (2024). Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures. BMC Infectious Diseases. 24(1). 610–610.

Ren, Boyu, et al.. (2024). Multi-study R -learner for estimating heterogeneous treatment effects across studies using statistical machine learning. Biostatistics. 26(1).

Dias, Daniel Penteado Martins, et al.. (2024). Identifying structural risk factors for overdose following incarceration: a concept mapping study. Health & Justice. 12(1). 11–11.

Patil, Prasad, et al.. (2024). Biological and Chemical Factors Influencing Food Allergies: A Comprehensive Review. Journal of Scientific Research and Reports. 30(6). 787–794. 1 indexed citations

Patil, Prasad, Jonathan I. Levy, Keith R. Spangler, et al.. (2023). Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts. Journal of Community Health. 49(1). 91–99. 3 indexed citations

Patil, Prasad, et al.. (2023). Speech Emotion Recognition Using Deep Learning. International Journal for Research in Applied Science and Engineering Technology. 11(5). 4829–4833. 2 indexed citations

Spangler, Keith R., Prasad Patil, Jonathan I. Levy, et al.. (2021). Community predictors of COVID‐19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data. Influenza and Other Respiratory Viruses. 16(2). 213–221. 5 indexed citations

Zhang, Yuqing, et al.. (2021). Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference. Biostatistics. 24(3). 635–652. 15 indexed citations

10.

Tao, Jun, Madeline C. Montgomery, Robert Williams, et al.. (2021). Loss to Follow-Up and Re-Engagement in HIV Pre-Exposure Prophylaxis Care in the United States, 2013–2019. AIDS Patient Care and STDs. 35(7). 271–277. 15 indexed citations

11.

Tieskens, Koen F., Prasad Patil, Jonathan I. Levy, et al.. (2021). Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts. BMC Infectious Diseases. 21(1). 686–686. 24 indexed citations

12.

Zhang, Yuqing, Prasad Patil, W. Evan Johnson, & Giovanni Parmigiani. (2020). Robustifying genomic classifiers to batch effects via ensemble learning. Bioinformatics. 37(11). 1521–1527. 10 indexed citations

13.

Westerman, Kenneth E., Alba Fernández‐Sanlés, Prasad Patil, et al.. (2020). Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics. Journal of the American Heart Association. 9(8). e015299–e015299. 27 indexed citations

14.

Bishara, Andrew, Susanna W.L. de Geus, Prasad Patil, et al.. (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. 35(1). 182–191. 61 indexed citations

15.

Patil, Prasad, et al.. (2017). Metagenomics: tools and techniques for data analysis. International Journal of Pharma and Bio Sciences. 8(3). 1 indexed citations

16.

Patil, Prasad, Elizabeth Colantuoni, Jeffrey T. Leek, & Michael Rosenblum. (2016). Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments. Contemporary Clinical Trials Communications. 3. 48–54. 1 indexed citations

17.

Patil, Prasad, et al.. (2015). Test set bias affects reproducibility of gene signatures. Bioinformatics. 31(14). 2318–2323. 74 indexed citations

18.

Wall, Dennis P., Parul Kudtarkar, Vincent A. Fusaro, et al.. (2010). Cloud computing for comparative genomics. BMC Bioinformatics. 11(1). 259–259. 81 indexed citations

19.

Joshi, Bhrugesh, et al.. (2009). Study of pectinase production in submerged fermentation using different strains of Aspergillus Niger. International Journal of Microbiology Research. 1(2). 13–17. 37 indexed citations

20.

Gupta, Mayank, et al.. (2009). Orange: Research analysis for wine study. 1(2). 10–15. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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