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Publications of Sayan Mukherjee    :chronological  combined  bibtex listing:

Books

  1. Mukherjee, SP; Sinha, BK; Chattopadhyay, AK, Statistical methods in social science research (October, 2018), pp. 1-152, ISBN 9789811321450 [doi]  [abs]

Papers Published

  1. A. Potti, S. Mukherjee, R. Petersen, HK. Dressman, A. Bild, J. Koontz, R. Kratzke, MA. Watson, M. Kelley, A Genomic Strategy to Refine Prognosis in Early Stage Non-Small Cell Lung Carcinoma, New England Journal of Medicine, vol. 355 no. 6 (2006), pp. 570-580 [pdf]
  2. Darnell, G; Georgiev, S; Mukherjee, S; Engelhardt, BE, Adaptive randomized dimension reduction on massive data, Journal of machine learning research : JMLR, vol. 18 (November, 2017)  [abs]
  3. R. Rifkin, S. Mukherjee, P. Tamayo, S. Ramaswamy, CH. Yeang, M. Reich, T. Poggio, ES. Lander, TR. Golub, JP. Mesirov, An Analytical Method for Multi-Class Cancer Classification, SIAM Reviews, vol. 45 no. 4 (Winter, 2003), pp. 706-723 [html]
  4. Sweet-Cordero, A., Mukherjee, S., You, H., Subramnian, S., Ladd, C., Roix, J., Mesirov, J.P., Golub, T.R., Jacks, T, An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis, Nature Genetics, vol. 37 no. 1 (January, 2005), pp. 48-55 [html]
  5. Elena Edelman, Alessandro Porrello, Justin Guinney, BalaBalakumaran, Andrea Bild, Phillip G. Febbo, and Sayan Mukherjee, Analysis of Sample Set Enrichment Scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles, Bioinformatics, vol. 22 no. 14 (2006), pp. e101-e116 [e108]
  6. R. Berger, PG. Febbo, PK. Majumder, JJ. Zhao, S. Mukherjee, T Campbell, WR. Sellers, TM. Roberts, M. Loda, TR. Golub, WC. Hahan, Androgen-Induced Differentiation and Tumorigenicity of Human Prostate Epithelial Cells, Cancer Research, vol. 64 (December, 2004), pp. 8867-8875 [8867]
  7. Crawford, L; Wood, KC; Zhou, X; Mukherjee, S, Bayesian Approximate Kernel Regression With Variable Selection, Journal of the American Statistical Association (August, 2017), pp. 1-12 [doi]
  8. Zhao, S; Gao, C; Mukherjee, S; Engelhardt, BE, Bayesian group factor analysis with structured sparsity, Journal of machine learning research : JMLR, vol. 17 (April, 2016), pp. 1-47  [abs]
  9. Peshkin, L; Mukherjee, S, Bounds on sample size for policy evaluation in Markov environments, Lecture notes in computer science, vol. 2111 (January, 2001), pp. 616-629, ISSN 0302-9743  [abs]
  10. Natesh Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert, Characterizing the function space for Bayesian kernel models, Journal of Machine Learning Research, vol. 8 (August, 2007), pp. 1769--1797 [html]  [abs]
  11. O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, Choosing Multiple Parameters for Support Vector Machines, Machine Learning, vol. 46 no. 1-3 (March, 2001), pp. 131-159 [contribution.asp]
  12. Stewart, L; MacLean, EL; Ivy, D; Woods, V; Cohen, E; Rodriguez, K; McIntyre, M; Mukherjee, S; Call, J; Kaminski, J; Miklósi, Á; Wrangham, RW; Hare, B, Citizen Science as a New Tool in Dog Cognition Research., PloS one, vol. 10 no. 9 (January, 2015), pp. e0135176 [doi]  [abs]
  13. Barish, S; Nuss, S; Strunilin, I; Bao, S; Mukherjee, S; Jones, CD; Volkan, PC, Combinations of DIPs and Dprs control organization of olfactory receptor neuron terminals in Drosophila., Plos Genetics, vol. 14 no. 8 (August, 2018), pp. e1007560 [doi]  [abs]
  14. Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA, Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts., Microbiome, vol. 6 no. 1 (November, 2018), pp. 212 [doi]  [abs]
  15. Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation., The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology (October, 2017) [doi]  [abs]
  16. Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA, Dynamic linear models guide design and analysis of microbiota studies within artificial human guts., Microbiome, vol. 6 no. 1 (November, 2018), pp. 202 [doi]  [abs]
  17. Snyder-Mackler, N; Majoros, WH; Yuan, ML; Shaver, AO; Gordon, JB; Kopp, GH; Schlebusch, SA; Wall, JD; Alberts, SC; Mukherjee, S; Zhou, X; Tung, J, Efficient Genome-Wide Sequencing and Low-Coverage Pedigree Analysis from Noninvasively Collected Samples., Genetics, vol. 203 no. 2 (June, 2016), pp. 699-714 [doi]  [abs]
  18. S. Mukherjee, P. Tamayo , S. Rogers, R. Rifkin, A. Engle, C. Campbell, TR. Golub, JP. Mesirov, Estimating Dataset Size Requirements for Classifying DNA Microarray Data, Journal of Computational Biology, vol. 10 no. 2 (April, 2003), pp. 119-142 [10.1089%2F106652703321825928]
  19. Berchuck, SI; Mukherjee, S; Medeiros, FA, Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder., Scientific Reports, vol. 9 no. 1 (December, 2019), pp. 18113 [doi]  [abs]
  20. S. Mukherjee and Q. Wu, Estimation of Gradients and Coordinate Covariation in Classification, Journal of Machine Learning Research, vol. 7 (November, 2006), pp. 2481--2514 [html]
  21. Zhong Wang, Huntington F. Willard, Sayan Mukherjee, Terrence S. Furey, Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation, Public Library of Science Computational Biology, vol. 2 no. 9 (Winter, 2006), pp. 979-988 [available here]
  22. Galinsky, KJ; Bhatia, G; Loh, P-R; Georgiev, S; Mukherjee, S; Patterson, NJ; Price, AL, Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia., The American Journal of Human Genetics, vol. 98 no. 3 (March, 2016), pp. 456-472 [doi]  [abs]
  23. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, Feature Selection for SVMs, in Proceedings of Advances in Neural Information Processing Systems, vol. 14 (2001), pp. 668-674
  24. Daniela Tropea, Gabriel Kreiman, Alvin Lyckman, Sayan Mukherjee, Hongbo Yu, Sam Horng and Mriganka Sur, Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex, Nature Neuroscience, vol. 9 (2006), pp. 660-668 [html]
  25. Jen-Tsan Chi1, Edwin H. Rodriguez, Zhen Wang, Dimitry S. A. Nuyten, Sayan Mukherjee, Matt van de Rijn, Marc J. van de Vijver, Trevor Hastie, Patrick O. Brown, Gene Expression Programs of Human Smooth Muscle Cells: Tissue-Specific Differentiation and Prognostic Significance in Breast Cancers, PLoS Genet, vol. 3 no. 9 (September, 2007), pp. 1770-1784 [pdf]
  26. A. Subramanian, P. Tamayo, VK. Mootha, S. Mukherjee, BL. Ebert, MA. Gillette, A. Paulovich, SL. Pomeroy, TR. Golub, ES. Lander, JP. Mesirov, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, PNAS, vol. 102 no. 43 (October, 2005), pp. 15278-9 [15545]
  27. Liang Goh, Susan K. Murphy, Sayan Muhkerjee, and Terrence S. Furey, Genomic sweeping for hypermethylated genes, Bioinformatics, vol. 23 no. 3 (February, 2007), pp. 281-288 [btl620v1]
  28. Cakir, M; Mukherjee, S; Wood, KC, Label propagation defines signaling networks associated with recurrently mutated cancer genes., Scientific Reports, vol. 9 no. 1 (June, 2019), pp. 9401 [doi]  [abs]
  29. S. Mukherjee, DX. Zhou, Learning Coordinate Covariances via Gradients, Journal of Machine Learning Research, vol. 7 (March, 2006), pp. 519-549 [html]
  30. T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi, Learning Theory: general conditions for predictivity, Nature, vol. 428 (March, 2004), pp. 419-422 [html]
  31. Singleton, KR; Crawford, L; Tsui, E; Manchester, HE; Maertens, O; Liu, X; Liberti, MV; Magpusao, AN; Stein, EM; Tingley, JP; Frederick, DT; Boland, GM; Flaherty, KT; McCall, SJ; Krepler, C; Sproesser, K; Herlyn, M; Adams, DJ; Locasale, JW; Cichowski, K; Mukherjee, S; Wood, KC, Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence., Cell Reports, vol. 21 no. 10 (December, 2017), pp. 2796-2812 [doi]  [abs]
  32. CH Yeang, S. Ramaswamy, P. Tamayo, S. Mukherjee, R. Rifkin, M. Angelo, M. Reich, E. Lander, J. Mesirov, T. Golub, Molecular classification of multiple tumor types, Bioinformatics, vol. 1 no. 1 (2001), pp. 1-7 [S316]
  33. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, CH Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, JP. Mesirov, T. Poggio, W. Gerald, M. Loda,, ES. Lander, TR. Golub, Multiclass cancer diagnosis using tumor gene expression signatures, PNAS, vol. 98 no. 26 (December, 2001), pp. 15149-15154 [15149]
  34. Pontil, M; Mukherjee, S; Girosi, F, On the noise model of support vector machines regression, in Proceedings of Algorithmic Learning Theory 11th Conference, Lecture notes in computer science, vol. 1968 (2000), pp. 316-324, Springer, Berlin, ISBN 9783540412373  [abs]
  35. LD. Miller, PM. Long,L. Wong, S. Mukherjee, LM. McShane, ET. Liu, Optimal gene expression analysis by microarrays, Cancer Cell, vol. 2 (November, 2002), pp. 353-361 [abstract]
  36. Tan, Z; Mukherjee, S, Partitioned tensor factorizations for learning mixed membership models, 34th International Conference on Machine Learning, Icml 2017, vol. 7 (January, 2017), pp. 5156-5165, ISBN 9781510855144  [abs]
  37. P. Golland, F. Liang, S. Mukherjee, D. Panchenko, Permutation Tests for Classification, in Proceedings of Computational Learning Theory 2005, edited by P. Auer and R. Meir (2005), pp. 501-515, Springer-Verlag
  38. Turner, K; Mukherjee, S; Boyer, DM, Persistent homology transform for modeling shapes and surfaces, Information and Inference, vol. 3 no. 4 (January, 2014), pp. 310-344 [doi]  [abs]
  39. Washburne, AD; Silverman, JD; Morton, JT; Becker, DJ; Crowley, D; Mukherjee, S; David, LA; Plowright, RK, Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data, Ecological Monographs (January, 2019) [doi]  [abs]
  40. Crawford, L; Monod, A; Chen, AX; Mukherjee, S; Rabadán, R, Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  41. Mukherjee, N; Mukherjee, S, Predicting signal peptides with support vector machines, Lecture notes in computer science, vol. 2388 (January, 2002), pp. 1-7, ISBN 354044016X [doi]  [abs]
  42. S. Pomeroy, P. Tamayo, M. Gaasenbeek, L. Sturla, M. Angelo, M. McLaughlin, J. Kim, L. Goumnerova, P. Black, C. Lau, J. Allen, D. Zigzag, J. Olson, T. Curran, C. Wetmore, J. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D. Louis, Prediction of central nervous system embryonal tumour outcome based of gene expression, Nature, vol. 415 no. 24 (January, 2002), pp. 436-442 [html]
  43. Munch, E; Turner, K; Bendich, P; Mukherjee, S; Mattingly, J; Harer, J, Probabilistic Fréchet means for time varying persistence diagrams, Electronic Journal of Statistics, vol. 9 no. 1 (January, 2015), pp. 1173-1204 [repository], [doi]  [abs]
  44. A. Rakhlin, D. Panchenko, S. Mukherjee, Risk Bounds for Mixture Density Estimation, ESAIM: Probability and Statistics, vol. 9 (June, 2005), pp. 220-229
  45. Tan, Z; Roche, K; Zhou, X; Mukherjee, S, Scalable algorithms for learning high-dimensional linear mixed models, 34th Conference on Uncertainty in Artificial Intelligence 2018, Uai 2018, vol. 1 (January, 2018), pp. 259-268, ISBN 9781510871601  [abs]
  46. A. Rakhlin, S. Mukherjee, T. Poggio, Stability Results In Learning Theory, Analysis and Applications, vol. 3 no. 4 (2005), pp. 397–417 [html]
  47. S. Mukherjee, P. Niyogi, T. Poggio, R. Rifkin, Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization, Advances in Computational Mathematics, vol. 25 no. 1-3 (2005), pp. 161 - 193 [contribution.asp]
  48. V. Vapnik, S. Mukherjee, Support vector method for multivariate density estimation, in Proceedings of Advances in Neural Information Processing Systems, edited by S. A. Solla, T. K. Leen, and K.R. Muller, vol. 12 (2000), pp. 659--665
  49. Gao, T; Brodzki, J; Mukherjee, S, The Geometry of Synchronization Problems and Learning Group Actions, Discrete & Computational Geometry (January, 2019) [doi]  [abs]
  50. Raskutti, G; Mukherjee, S, The information geometry of mirror descent, Lecture notes in computer science, vol. 9389 (January, 2015), pp. 359-368, ISBN 9783319250397 [doi]  [abs]
  51. Bobrowski, O; Mukherjee, S; Taylor, JE, Topological consistency via kernel estimation, Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, vol. 23 no. 1 (February, 2017), pp. 288-328 [doi]
  52. F. Liang, S. Mukherjee, M. West, Understanding the use of unlabelled data in predictive modelling, Statistical Science, vol. 22 no. 2 (Fall, 2007), pp. 189-205
  53. Bonnefoi H, Potti A, Delorenzi M, Mauriac L, Campone M, Tubiana-Hulin M, Petit T, Rouanet P, Jassem J, Blot E, Becette V, Farmer P, André S, Acharya CR, Mukherjee S, Cameron D, Bergh J, Nevins JR, Iggo RD., Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG 00-01 clinical trial., Lancet Oncology, vol. 8 no. 12 (December, 2007), pp. 1071-1078 [entrez]

Papers Accepted

  1. E. Edelman, J. Guinney, J-T. Chi, P.G. Febbo, and S. Mukherjee, Modeling Cancer Progression via Pathway Dependencies, Public Library of Science Computational Biology (2007) [html]

Papers Submitted

  1. J. Guinney, Q. Wu, and S. Mukherjee, Estimating variable structure and dependence in Multi-task learning via gradients, Journal of Machine Learning Research (2007) [html]
  2. S. Mukherjee, Q. Wu, D-X. Zhou, Learning Gradients and Feature Selection on Manifolds, Annals of Statistics (2007) [html]
  3. Q. Wu, J. Guinney, M. Maggioni, and S. Mukherjee, Learning gradients: predictive models that infer geometry and dependence, Journal of Machine Learning Research (2007) [html]
  4. F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West, Non-parametric Bayesian kernel models, Biometrika (2007) [html]
  5. Q. Wu, S. Mukherjee, F. Liang, Regularized sliced inverse regression for kernel models., Biometrika (2007) [html]

Chapters

  1. Huang, B; Jarrett, NWD; Babu, S; Mukherjee, S; Yang, J, Cümülön: MatrixBased data analytics in the cloud with spot instances, in Proceedings of the VLDB Endowment, vol. 9 (January, 2016), pp. 156-167  [abs]

 

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