2006:

Soni, Neha (S.M. Thesis, EECS, MIT, February 2006): Sequence Motifs Predictive of Tissue-specific Skipping.

2005:

Han, K., G. Yeo, P. An, C.B. Burge and P.J. Grabowski. A Combinatorial Code for Splicing Silencing: UAGG and GGGG Motifs, PLoS Biology, Vol. 3, Issue 5, e158, 0001-0018, May 2005.

Sweet-Cordero, A., S. Mukherjee, A. Subramanian, H. You, J.J. Roix, C. Ladd-Acosta, J. Mesirov, T.R. Golub and T. Jacks. An Oncogenic KRAS2 Expression Signature Identified by Cross-species Gene-expression Analysis, Nature Genetics, 37, 1, 48-55, 2005.

Yeo, G., E. Van Nostrand, D. Holste, T. Poggio and C.B. Burge. Identification and Analysis of Alternative Splicing Events Conserved in Human and Mouse, Proceedings of the National Academy of Sciences (PNAS), 102, 8, 2850-2855, 2005.

2004:

Crick, F., C. Koch, G. Kreiman and I. Fried. Consciousness and Neurosurgery, Neurosurgery, 55, 272-282, 2004.

Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004.

Kreiman, G. Identification of Sparsely Distributed Clusters of Cis-Regulatory Elements in Sets of Co-expressed Genes, Nucleic Acids Research, 32, 2889-2900, 2004.

Su, A.I., T. Wiltshire, S. Batalov, H. Lapp, K.A. Ching, D. Block, J. Zhang, R. Soden, M. Hayakawa, G. Kreiman, M.P. Cooke, J.R. Walker and J.B. Hogenesc. A Gene Atlas of the Mouse and Human Protein-Encoding Transcriptomes," Proceedings of the National Academy of Sciences USA, 101, 6062-6067, 2004.

Wang, Z., M.E. Rolish, G. Yeo, V. Tung, M. Mawson and C.B. Burge. Systematic Identification and Analysis of Exonic Splicing Silencers, Cell, 119, 831-845, 2004.

Wolf, L., A. Shashua and S. Mukherjee. Selecting Relevant Genes with a Spectral Approach, CBCL Paper #234/AI Memo #2004-002, Massachusetts Institute of Technology, Cambridge, MA, January, 2004.

Yeo, Gene W. (Ph.D. Thesis, EECS, MIT, November 2004): Identification, Improved Modeling and Integration of Signals to Predict Constitutive and Alternative Splicing.

Yeo, G., D. Holste, G. Kreiman and C. Burge. Variation in Alternative Splicing across Human Tissues, Genome Biology, 5, R74, 2004.

Yeo, G., S. Hoon, B. Venkatesh and C. Burge. Variation in Sequence and Organization of Splicing Regulatory Elements in Vertebrate Genes, Proceedings of the National Academy of Sciences (PNAS), 191(44), 15700-15705, 2004.

2003:

Dror, R.O., J.G. Murnick, N.A. Rinaldi, V.D. Marinescu, R.M. Rifkin and R.A. Young. Bayesian Approach to Transcript Estimation from Gene Array Data: The BEAM Technique. In: Proceedings of the Sixth Annual International Conference on Research in Computational Molecular Biology, Washington, D.C., April 2002, in press.

Mukherjee, S. Classifying Microarray Data Using Support Vector Machines. In: A Practical Approach to Microarray Data Analysis, D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), Kluwer Academic Publishers, Boston, MA, Chapter 9, 166-185, 2003.

Mukherjee, S., P. Golland and D. Panchenko. Permutation Tests for Classification, AI Memo #2003-019, Massachusetts Institute of Technology, Cambridge, MA, August 2003.

Mukherjee, S., P. Tamayo, S. Rogers, R. Rifkin, A. Engle, C. Campbell, T.R. Golub and J.P. Mesirov. Estimating Dataset Size Requirements for Classifying DNA Microarray Data, Journal of Computational Biology, 2003, in press.

Rifkin, R., S. Mukherjee, P. Tamayo, S. Ramaswamy, C.-H. Yeang, M. Angelo, M. Reich, T. Poggio, E.S. Lander, T.R. Golub and J.P. Mesirov. An Analytical Method for Multi-class Molecular Cancer Classification, SIAM Reviews Vol. 45, No. 4, 706-723, 2003.

Rifkin, R., G. Yeo and T. Poggio. Regularized Least Squares Classification. In: Advances in Learning Theory: Methods, Model and Applications, NATO Science Series III: Computer and Systems Sciences, VIOS Press, Amsterdam, (Eds.) Suykens, Horvath, Basu, Micchelli and Vandewalle, Vol. 190, Chapter 7, 131-154, 2003.

Yeo, G. and C. Burge. Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals. In: Proceedings of the Seventh Annual International Conference on Research in Computational Molecular Biology, Berlin, Germany, April 10-13, 2003.

2002:

Miller, L.D., P.M. Long, L. Wong, S. Mukherjee, L.M. McShane and E.T. Liu. Optimal Gene Expression Analysis by Microarrays, Cancer Cell, Vol. 2, 353-361, 2002.

Pomeroy, S.L., P. Tamayo, M. Gaasenbeek, L.M. Sturia, M. Angelo, M.E. McLaughlin, J.Y.H. Kim, L.C. Goumnerova, P.M. Black, C. Lau, J.C. Allen, D. Zagzag, M.M. Olson, T. Curran, C. Wetmore, J.A. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D.N. Louis, J.P. Mesirov, E.S. Lander and T.R. Golub. Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression, Nature (Letters to Nature), Vol. 415, 436-442, 2002.

Rifkin, R. (Ph.D. Thesis, EECS & OR, MIT, September, 2002): Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning.

2001:

Mukherjee, Sayan (Ph.D. Thesis, BCS, MIT, June 2001): Application of Statistical Learning Theory to DNA Microarray Analysis.

Ramaswamy, S., P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E.S. Lander, and T.R. Golub. Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures, PNAS, Vol. 98, No. 26, 15149-15154, December 2001.

Yeang, C.-H., S. Ramaswamy, P. Tamayo, S. Mukherjee, R.M. Rifkin, M. Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub. Molecular Classification of Multiple Tumor Types. In: Intelligent Systems in Molecular Biology - Bioinformatics Discovery Note, Proceedings of the Ninth International Conference on Intelligent Systems in Molecular Biology, Copenhagen, Denmark, (July 21-25, 2001), Vol. 1, No. 1, S316-S322, 2001.

Yeo, G. and T. Poggio. Multiclass Classification of SRBCTs, CBCL Paper #204/AI Memo #2001-018, Massachusetts Institute of Technology, Cambridge, MA, August 2001.

2000:

Chapelle, O., V. Vapnik, O. Bousquet and S. Mukherjee. "Choosing Kernel Parameters for Support Vector Machines," Machine Learning - Special Issue on Support Vector Machines, 2000 (to appear).

Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. Vapnik. Feature Selection for SVMs. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), 13, 668-674, 2001.

1999:

Mukherjee, S., P. Tamayo, J.P. Mesirov, D. Slonim, A. Verri and T. Poggio. Support Vector Machine Classification of Microarray Data, CBCL Paper #182/AI Memo #1677, Massachusetts Institute of Technology, Cambridge, MA, December 1999.