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
Design 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
ACM SIGKDD (largest society of professional data mining researchers)
Structure extraction from unstructured documents, US Patent 7,562,088
Doctoral dissertation fellowship
University of Minnesota Graduate School
ACM SIGMOD (largest society of professional database researchers and practitioners)
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).
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.
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Dr.Pandey did not report having any of the following types of financial relationships with industry during 2015 and/or 2016: 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.
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