For an updated list of our publications, along with citation statistics, please visit our Google Scholar page.

PDFs of articles published in non-open access venues are being provided to facilitate dissemination. However, copyrights held by the respective publishers must be respected.

2019

• Rosenstein B, Pandey G, Speers CW, Oh JH, West CML, Mayo CS. Radiogenomics. Book chapter in Big Data in Radiation Oncology, Editors Deng J and Xing L, CRC Press.
• Varghese B, Chen F, Hwang D, Palmer SL, Abreu ALDC, Ukimura O, Aron M, Aron M, Gill I, Duddalwar V, Pandey G. Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Scientific Reports 9: 1570 (Mount Sinai press release).

2018

• El Naqa I, Pandey G, Aerts H, Chien J-T, Andreassen CN, Niemierko A, Haken RKT. Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation. International Journal of Radiation Oncology*Biology*Physics 102(4):1070-1073 (pdf).
• Fourati S, Talla A, Mahmoudian M, Burkhart JG, Klen R, Henao R, Aydin Z, Yeung KY, Ahsen ME, (6 authors), Stanescu A, Vogel R, The Respiratory Viral DREAM Challenge Consortium, Pandey G, (6 authors), Mangravite LM, Sieberts SK. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications 9:4418.
• Wang L, Law J, Kale SD, Murali TM, Pandey G. Large-scale protein function prediction using heterogeneous ensembles. F1000 Research 7(ISCB Comm J):1577.
• Pandey G, Pandey OP, Rogers AJ, Ahsen ME, Hoffman GE, Raby BA, Weiss ST, Schadt EE, Bunyavanich S. A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data. Scientific Reports 8: 8826 (Mount Sinai press release, GEN article, complete press coverage).
• Stanescu A, Pandey G. Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework. arXiv:1805.02103.

2017

• Stingone JA, Pandey OP, Claudio L, Pandey G. Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children. Environmental Pollution 230 (2017): 730-740.
• Carcamo-Orive I, Hoffman GE, Cundiff P, Beckmann ND, (6 authors), Whalen S, (7 authors), Pandey G, Chang R, Quertermous T, Lemischka I. Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell Stem Cell 20(4): 518–532.e9. (pdf)
• Stanescu A, Pandey G. Learning parsimonious ensembles for unbalanced computational genomics problems. Proceedings of Pacific Symposium on Biocomputing (PSB), 22: 288-299. (Software implementation LENS)

2016

• Sieberts SK, Zhu F, Garcia-Garcia J, Stahl E, Pratap A, Pandey G, Pappas D, Aguilar D, Anton Bernat, et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications 7:12460.
• Evrard SM, Lecce L, Michelis KC, Nomura-Kitabayashi A, Pandey G, Purushothaman K-R, d’Escamard V, Li JR, Hadri L, Fujitani K, et al. Endothelial to mesenchymal transition is common in atherosclerotic lesions and is associated with plaque instability. Nature Communications 7:11853.
• Whalen S, Pandey OP, Pandey G. Predicting protein function and other biomedical characteristics with heterogeneous ensembles. Methods 93(15):92-102. (pdf) (Software implementation: DataSink)
• Margolies LR, Pandey G, Horowitz ER, Mendelson DS. Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining. American Journal of Roentgenology 206: 259-264. (pdf)
• Ruane D, Chorny A, Lee H, Faith J, Pandey G, Shan M, Simchoni N, Rahman A, Garg A, Weinstein EG, et al. Microbiota regulate the ability of lung dendritic cells to induce IgA class-switch recombination and generate protective gastrointestinal immune responses. Journal of Experimental Medicine 213(1):53-73. (pdf)

2015

• Madhukar NS, Elemento O, Pandey G. Prediction of genetic interactions using machine learning and network properties. Frontiers in Bioengineering and Biotechnology 3:172.

2014

• Pandey G, Arora S, Manocha S, Whalen S. Enhancing the functional content of eukaryotic protein interaction networks. PLoS One 9(10): e109130.
• Pandey G, Rangwala H. Guest editorial for special section on BIOKDD2013. IEEE/ACM Transactions on Computational Biology and Bioinformatics 11(5): 773-774.

2013

• Whalen S, Pandey G. A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics. Proceedings of the IEEE International Conference on Data Mining, pp. 807-816. (pdf) (Software implementation DataSink)
• Bilal E*, Dutkowski J*, Guinney J*, Jang IS*, Logsdon BA*, Pandey G*, Sauerwine BA*, Shimoni Y*, Moen Vollan HK, Mecham BH, et al. Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS computational biology 9(5):e1003047 (* Equal contribution).
• Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, et al. A large-scale evaluation of computational protein function prediction. Nature Methods 10(3):221-227.
• Pandey G, Zhang B, Jian L. Predicting submicron air pollution indicators: a machine learning approach. Environmental Science: Processes & Impacts 15:996-1005. (pdf)
• Pandey G, Cohain A, Miller J, Merad M. Decoding dendritic cell function through module and network analysis. Journal of Immunological Methods 387(1-2):71-80. (pdf)

2012

• Pandey G, Manocha S, Atluri G, Kumar V. Enhancing the functional content of protein interaction networks. arXiv preprint 1210.6912.
• Miller JC, Brown BD, Shay T, Gautier EL, Jojic V, Cohain A, Pandey G, Leboeuf M, Elpek KG, Helft J, et al. Deciphering the transcriptional network of the dendritic cell lineage. Nature Immunology 13(9):888-899. (pdf)
• Fang G, Pandey G, Wang W, Gupta M, Steinbach M, Kumar V. Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data. IEEE Transactions on Knowledge and Data Engineering 24(2):279-294. (pdf)

2011

• Bellay J, Atluri G, Sing TL, Toufighi K, Costanzo M, Ribeiro PS, Pandey G, Baller J, VanderSluis B, Michaut M, Han S, Kim P, et al. Putting genetic interactions in context through a global modular decomposition. Genome Research 21:1375-1387.

2010

• 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 6(9):e1000928.
• Fang G, Kuang R, Pandey G, Steinbach M, Myers CL, Kumar V. Subspace differential coexpression analysis: problem definition and a general approach. Proceedings of the Pacific Symposium on Biocomputing, pp 145-156.

2009

• Pandey G, Myers CL, Kumar V. Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinformatics 10:142.
• Pandey G, Atluri G, Steinbach M, Myers CL, Kumar V. An association analysis approach to biclustering. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), pp 677-686. (pdf)
• Pandey G, Chawla S, Poon S, Arunasalam B, Davis JG. Association Rules Network: Definition and Applications Statistical Analysis and Data Mining 1(4):260-279. (pdf)
• Atluri G, Gupta R, Fang G, Pandey G, Steinbach M, Kumar V. Association Analysis Techniques for Bioinformatics Problems. In Bioinformatics and Computational Biology, Lecture Notes in Computer Science, pp. 1-13. (pdf)

2008

• Pandey G, Ramakrishnan LN, Steinbach M, Kumar V. Systematic Evaluation of Scaling Methods for Gene Expression Data. Proceedings of the IEEE International Conference on BioInformatics and BioMedicine (BIBM), pp. 376-381. (pdf)

2007

• Pandey G, Steinbach M, Gupta R, Garg T, Kumar V. Association analysis-based transformations for protein interaction networks: a function prediction case study. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), pp 540-549. (pdf)