Gaurav Pandey, PhD
- ASSISTANT PROFESSOR | Genetics and Genomic Sciences
Research Topics:Bioinformatics, Biostatistics, Computational Biology, Drug Design and Discovery, Epigenetics, Gene Regulation, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Microarray, Oncogenes, Protein Structure/Function
Gaurav Pandey is an Assistant Professor in the Department of Genetics and Genomic Sciences at the Mount Sinai School of Medicine (New York) and is part of the newly formed Institute for Genomics and Multiscale Biology. He completed his Ph.D. in computer science and engineering from the University of Minnesota, Twin Cities in 2010, and subsequently completed a post-doctoral fellowship at the University of California, Berkeley. His primary fields of interest are computational biology, genomics and large-scale data analysis and mining, and he has published extensively in these areas.
Multi-Disciplinary Training AreasDesign Technology and Entrepreneurship [DTE], Genetics and Genomic Sciences [GGS]
PhD, University of Minnesota
Post-doctoral scholar, University of California, Berkeley
IBM Faculty Award
Certificate of recognition for excellent contributions to data mining research
Structure extraction from unstructured documents, US Patent 7,562,088
Doctoral dissertation fellowship
Finalist for Ph.D. fellowship (one of about 30 candidates across all Comp. Sc. departments)
Computational genomics and large-scale data analysis/mining
Our lab develops and applies computational methods for building predictive and network models of complex biological processes and diseases. These data-driven methods have become critical in the new era biology, which is witnessing an explosion of the amount and types of data like never before. Using such approaches, we have gained successful insights into immunological processes, large-scale interactions between genes and proteins and survival of breast cancer patients. We also have a continuing interest in the computational prediction of protein function and the development of novel data mining and machine learning methods. For more details of our work, check out our lab website.
We have built successful collaborations with immunologists and cancer and cardiovascular specialists, with many more being planned. We are a small lab right now, because of which the PI can pay more attention to lab members' projects and problems. We are a completely computational or bioinformatics lab right now, so some computing experience will be appreciated, but not necessary. The main requirement is a strong motivation to learn and excel. If you are interested in joining us (we hope you are!), send an email to email@example.com to set up a time for discussion. We are located within the Genomics Institute's suite on the 3rd floor of the Icahn Medical Institute.
Whalen S, Pandey OP, Pandey G. Predicting protein function and other biomedical characteristics with heterogeneous ensembles. Methods (San Diego, Calif.) 2015 Sep;.
Ruane D, Chorny A, Lee H, Faith J, Pandey G, Shan M, Simchoni N, Rahman A, Garg A, Weinstein EG, Oropallo M, Gaylord M, Ungaro R, Cunningham-Rundles C, Alexandropoulos K, Mucida D, Merad M, Cerutti A, Mehandru S. Microbiota regulate the ability of lung dendritic cells to induce IgA class-switch recombination and generate protective gastrointestinal immune responses. The Journal of experimental medicine 2016 Jan; 213(1).
Margolies LR, Pandey G, Horowitz ER, Mendelson DS. Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining. AJR. American journal of roentgenology 2016 Feb; 206(2).
Madhukar NS, Elemento O, Pandey G. Prediction of Genetic Interactions Using Machine Learning and Network Properties. Frontiers in bioengineering and biotechnology 2015; 3.
Pandey G, Arora S, Manocha S, Whalen S. Enhancing the functional content of eukaryotic protein interaction networks. PloS one 2014; 9(10).
Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, Sauerwine BA, Shimoni Y, Moen Vollan HK, Mecham BH, Rueda OM, Tost J, Curtis C, Alvarez MJ, Kristensen VN, Aparicio S, Børresen-Dale AL, Caldas C, Califano A, Friend SH, Ideker T, Schadt EE, Stolovitzky GA, Margolin AA. Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS computational biology 2013; 9(5).
Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, Yunes JM, Talwalkar AS, Repo S, Souza ML, Piovesan D, Casadio R, Wang Z, Cheng J, Fang H, Gough J, Koskinen P, Törönen P, Nokso-Koivisto J, Holm L, Cozzetto D, Buchan DW, Bryson K, Jones DT, Limaye B, Inamdar H, Datta A, Manjari SK, Joshi R, Chitale M, Kihara D, Lisewski AM, Erdin S, Venner E, Lichtarge O, Rentzsch R, Yang H, Romero AE, Bhat P, Paccanaro A, Hamp T, Kaßner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Björne J, Salakoski T, Wong A, Shatkay H, Gatzmann F, Sommer I, Wass MN, Sternberg MJ, Škunca N, Supek F, Bošnjak M, Panov P, Džeroski S, Šmuc T, Kourmpetis YA, van Dijk AD, ter Braak CJ, Zhou Y, Gong Q, Dong X, Tian W, Falda M, Fontana P, Lavezzo E, Di Camillo B, Toppo S, Lan L, Djuric N, Guo Y, Vucetic S, Bairoch A, Linial M, Babbitt PC, Brenner SE, Orengo C, Rost B, Mooney SD, Friedberg I. A large-scale evaluation of computational protein function prediction. Nature methods 2013 Mar; 10(3).
Pandey G, Zhang B, Jian L. Predicting submicron air pollution indicators: a machine learning approach. Environmental science. Processes & impacts 2013 Mar;.
Pandey G, Cohain A, Miller J, Merad M. Decoding dendritic cell function through module and network analysis. Journal of immunological methods 2012 Oct;.