Publications of Sayan Mukherjee     :chronological  combined  bibtex listing:

Papers Published

1. 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 8.12 (December, 2007): 1071-1078. [entrez]
2. F. Liang, S. Mukherjee, M. West. "Understanding the use of unlabelled data in predictive modelling." Statistical Science 22.2 (Fall, 2007): 189-205.
3. 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 3.9 (September, 2007): 1770-1784. [pdf]
4. Natesh Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert. "Characterizing the function space for Bayesian kernel models." Journal of Machine Learning Research 8 (August, 2007): 1769--1797. [html]  [abs]
5. Liang Goh, Susan K. Murphy, Sayan Muhkerjee, and Terrence S. Furey. "Genomic sweeping for hypermethylated genes." Bioinformatics 23.3 (February, 2007): 281-288. [btl620v1]
6. 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 2.9 (Winter, 2006): 979-988. [available here]
7. S. Mukherjee and Q. Wu. "Estimation of Gradients and Coordinate Covariation in Classification." Journal of Machine Learning Research 7 (November, 2006): 2481--2514. [html]
8. S. Mukherjee, DX. Zhou. "Learning Coordinate Covariances via Gradients." Journal of Machine Learning Research 7 (March, 2006): 519-549. [html]
9. 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 22.14 (2006): e101-e116. [e108]
10. 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 9 (2006): 660-668. [html]
11. 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 355.6 (2006): 570-580. [pdf]
12. 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 102.43 (October, 2005): 15278-9. [15545]
13. A. Rakhlin, D. Panchenko, S. Mukherjee. "Risk Bounds for Mixture Density Estimation." ESAIM: Probability and Statistics 9 (June, 2005): 220-229.
14. 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 37.1 (January, 2005): 48-55. [html]
15. A. Rakhlin, S. Mukherjee, T. Poggio. "Stability Results In Learning Theory." Analysis and Applications 3.4 (2005): 397–417. [html]
16. P. Golland, F. Liang, S. Mukherjee, D. Panchenko. "Permutation Tests for Classification." Proceedings of Computational Learning Theory 2005 (2005): 501-515.
17. 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 25.1-3 (2005): 161 - 193. [contribution.asp]
18. 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 64 (December, 2004): 8867-8875. [8867]
19. T. Poggio, R. Rifkin, S. Mukherjee, P. Niyogi. "Learning Theory: general conditions for predictivity." Nature 428 (March, 2004): 419-422. [html]
20. 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 45.4 (Winter, 2003): 706-723. [html]
21. 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 10.2 (April, 2003): 119-142. [10.1089%2F106652703321825928]
22. LD. Miller, PM. Long,L. Wong, S. Mukherjee, LM. McShane, ET. Liu. "Optimal gene expression analysis by microarrays." Cancer Cell 2 (November, 2002): 353-361. [abstract]
23. 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 415.24 (January, 2002): 436-442. [html]
24. 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 98.26 (December, 2001): 15149-15154. [15149]
25. O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee. "Choosing Multiple Parameters for Support Vector Machines." Machine Learning 46.1-3 (March, 2001): 131-159. [contribution.asp]
26. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik. "Feature Selection for SVMs." Proceedings of Advances in Neural Information Processing Systems 14 (2001): 668-674.
27. 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 1.1 (2001): 1-7. [S316]
28. M. Pontil, S. Mukherjee, F. Girosi. "On the Noise Model of Support Vector MachinesRegression." Proceedings of Algorithmic Learning Theory 11th Conference (2000): 316--324.
29. V. Vapnik, S. Mukherjee. "Support vector method for multivariate density estimation." Proceedings of Advances in Neural Information Processing Systems 12 (2000): 659--665.

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. Q. Wu, S. Mukherjee, F. Liang. "Regularized sliced inverse regression for kernel models.." Biometrika  (2007). [html]
2. F. Liang, K. Mao, M. Liao, S. Mukherjee and M. West. "Non-parametric Bayesian kernel models." Biometrika  (2007). [html]
3. 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]
4. 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]
5. S. Mukherjee, Q. Wu, D-X. Zhou. "Learning Gradients and Feature Selection on Manifolds." Annals of Statistics  (2007). [html]