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Gaurav Pandey

  • ASSISTANT PROFESSOR Genetics and Genomic Sciences
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Education

  • Ph.D., University of Minnesota
    Computer Science and Engineering

  • Post-doctoral scholar, University of California, Berkeley
    Computational Genomics

Biography

    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.

Awards

  • 2011 -
    Certificate of recognition for excellent contributions to data mining research
    ACM SIGKDD (largest society of professional data mining researchers)

  • 2009 - 2010
    Doctoral dissertation fellowship
    University of Minnesota Graduate School

  • 2009 -
    Structure extraction from unstructured documents, US Patent 7,562,088

  • 2004 -
    Finalist for Ph.D. fellowship (one of about 30 candidates across all Comp. Sc. departments)
    IBM

  • 2004 -
    Undergraduate scholarship
    ACM SIGMOD (largest society of professional database researchers and practitioners)

Research


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 publications at Google Scholar.

Our lab has been at Sinai for just over an year, during which time 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 gaurav.pandey@mssm.edu 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.

Publications

Whalen S, Pandey G. A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics. Proceedings of the IEEE International Conference on Data Mining 2013;: 807-816.

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;.

Miller JC, Brown BD, Shay T, Gautier EL, Jojic V, Cohain A, Pandey G, Leboeuf M, Elpek KG, Helft J, Hashimoto D, Chow A, Price J, Greter M, Bogunovic M, Bellemare-Pelletier A, Frenette PS, Randolph GJ, Turley SJ, Merad M. Deciphering the transcriptional network of the dendritic cell lineage. Nature immunology 2012 Sep; 13(9).

Bellay J, Atluri G, Sing TL, Toufighi K, Costanzo M, Ribeiro PS, Pandey G, Baller J, VanderSluis B, Michaut M, Han S, Kim P, Brown GW, Andrews BJ, Boone C, Kumar V, Myers CL. Putting genetic interactions in context through a global modular decomposition. Genome research 2011 Aug; 21(8).

Pandey G, Zhang B, Chang AN, Myers CL, Zhu J, Kumar V, Schadt EE. An integrative multi-network and multi-classifier approach to predict genetic interactions. PLoS computational biology 2010; 6(9).

Fang G, Kuang R, Pandey G, Steinbach M, Myers CL, Kumar V. Subspace differential coexpression analysis: problem definition and a general approach. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2010;.

Pandey G, Myers CL, Kumar V. Incorporating functional inter-relationships into protein function prediction algorithms. BMC bioinformatics 2009; 10.

Industry Relationships

Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.

Dr. Pandey did not report having any of the following types of financial relationships with industry during 2013 and/or 2014: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.

Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website at http://icahn.mssm.edu/about-us/services-and-resources/faculty-resources/handbooks-and-policies/faculty-handbook. Patients may wish to ask their physician about the activities they perform for companies.

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Address

Icahn Medical Institute Floor 3rd Room L3-70G
1425 Madison Avenue
New York, NY 10029