sroy8 [at] wisc.edu
PhD, Computer Science, University of New Mexico, 2009
My research focuses on developing statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets, that measure different aspects of the cellular state.
I am interested in identifying networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and constructing predictive models from these networks.
Specifically, some research topics of my interest are:
- Inference of structure and function of regulatory networks
- Comparative analysis of expression modules across species
- Evolution of gene regulation
- Relational learning to predict function
- Modeling condition-specific functional behavior
- Learning causal networks
- Predictive models of phenotypic response
S. Roy, T. Lane, M. Werner-Washburne (2011). A multiple network learning approach to capture system-wide condition-specific responses. Bioinformatics, 27;13
N. Rhind, Z. Chen, M. Yassour+, D. A. Thompson+, B. J. Haas+, N. Habib+, I. Wapinski+, S. Roy+, M. F. Lin, D. I. Heiman, et al. (2011). Comparative Functional Genomics of the Fission Yeasts. Science 332, 930. +Equal contribution
G. S. Davidson, R. M. Joe, S. Roy, O. Meirelles, C. P. Allen, M. R. Wilson, P. H. Tapia, E. E. Manzanilla, A. E. Dodson, S. Chakraborty, M. Carter, S. Young, B. Edwards, L. Sklar, and M. Werner-Washburne (2011). The proteomics of quiescent and non-quiescent cell differentiation in yeast stationary-phase cultures. Molecular Biology of the Cell.
The modENCODE Consortium, S. Roy+, J. Ernst+, P. V. Kharchenko+, P. Kheradpour+, N. Negre+, M. L. Eaton+, J.M. Landolin+, C. A. Bristow+, L. Ma+, M. F. Lin+, S. Washietl+, B. I. Arshinoff+, F. Ay+, P. E. Meyer+, N. Robine+, N. L. Washington+, L. D. Stefano+, E. Berezikov, C. D. Brown, R. Candeias, J. W. Carlson, A. Carr, I. Jungreis, D. Marbach, R. Sealfon, M. Y. Tolstorukov, S.Will, A. Alekseyenko, C. Artieri, B.W. Booth, A. N. Brooks, Qi. Dai, C. A. Davis, M. O. Duff, X. Feng, A. Gorchakov, T. Gu, J. G. Henikoff, P. Kapranov, R. Li, H. MacAlpine, J. Malone, A. Minoda, J. Nordman, K. Okamura, M. Perry, S. Powell, N. C. Riddle, A. Sakai, A. Samsonova, J. E. Sandler, Y. Schwartz, N. Sher, R. Spokony, D. Sturgill, M. van Baren, K. H. Wan, L.Yang, C. Yu, E. Feingold, P. Good, M. Guyer, R. Lowdon, K. Ahmad, J. Andrews, B. Berger, S. E. Brenner, M. R. Brent, L. Cherbas, S. C. R. Elgin, T. R. Gingeras, R. Grossman, R. A. Hoskins, T. C. Kaufman, W. Kent, M. Kuroda, T. Orr-Weaver, N. Perrimon, V. Pirrotta, J. W. Posakony, B. Ren, S. Russell, P. Cherbas, B. R. Graveley, S. Lewis, G. Micklem, B. Oliver, P. J. Park S. E. Celniker, S. Henikoff, G. H. Karpen, E. C. Lai, D. M. MacAlpine, L. D. Stein, K. P. White, and M. Kellis (2010). Identification of functional elements and regulatory circuits in Drosophila modENCODE. Science. +Co-first authors.
S. Roy, D. Martinez, H. Platero, T. Lane, M. Werner-Washburne (2009). Exploiting Amino Acid Composition for Predicting Protein-protein Interactions. PLoS ONE.
S. Roy, T. Lane, M. Werner-Washburne (2009). Learning structurally consistent undirected probabilistic graphical models. Proceedings of the 26th International Conference on Machine Learning.
S. Roy, S. Plis, M. Werner-Washburne, T. Lane (2009). Scalable learning of large networks. q-bio 2008 Special Issue for IET Systems Biology.
S. Roy, T. Lane, M. Werner-Washburne and D. Martinez (2009). Inference of functional networks of condition-specifc response - A case study of quiescence in yeast. Pacific Symposium of Biocomputing.
S. Roy, M. Werner-Washburne, and T. Lane (2008). A system for generating transcription regulatory networks with combinatorial control of transcription. Bioinformatics, 24(10).
A.D. Aragon, A. L. Rodriguez, O. Meirelles, S. Roy, G. S. Davidson, P. H. Tapia, C. Allen, R. Joe, D. Benn, and M. Werner-Washburne (2008). Characterization of differentiated quiescent and nonquiescent cells in yeast stationary-phase cultures. Mol. Biol. Cell, 19(3).
A. Stark, M. F. Lin, P. Kheradpour, J. S. Pedersen, L. Parts, J. W. Carlson, M. A. Crosby, M. D. Rasmussen, S. Roy, A. N. Deoras, J. G. Ruby, J. Brennecke, Harvard FlyBase curators, Berkeley Drosophila Genome Project, E. Hodges, A. S. Hinrichs, A. Caspi, B. Paten, S. Park, M. V. Han, M. L. Maeder, B. J. Polansky, B. E. Robson, S. Aerts, J. Helden, B. Hassan, D. G. Gilbert, D. A. Eastman, M. Rice, M. Weir, M. W. Hahn, Y. Park, C. N. Dewey, L. Pachter, W. J. Kent, D. Haussler, E. C. Lai, D. P. Bartel, G. J. Hannon, T. C. Kaufman, M. B. Eisen, A. G. Clark, D. Smith, S. E. Celniker, W. M. Gelbart, and M. Kellis (2007). Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures. Nature, 450.
P. Kheradpour, A. Stark, S. Roy, M. Kellis (2007). Reliable prediction of regulator targets using 12 Drosophila genomes. Genome Research, 17.
S. Roy, T. Lane, C. Allen, A. D. Aragon, M. Werner-Washburne (2006). A Hidden-state Markov Model for Cell Population Deconvolution. Journal of Computational Biology, 13(10).
P. D. Wentzell, T. K. Karakach, S. Roy, M. J. Martinez, C. P. Allen, M. Werner-Washburne (2006). Multivariate curve resolution of time course microarray data. BMC Bioinformatics, 7(343).
A. D. Aragon, G. A. Quinones, E. V. Thomas, S. Roy, G. S. Davidson, and M. Werner-Washburne (2006). Release of extraction-resistant mRNA in stationary phase Saccharomyces cerevisiae produces a massive increase in transcript abundance in response to stress. Genome Biology, 7(R9).
A. D. Aragon, G. A. Quinones, C. Allen, J. Thomas, S. Roy, G. S. Davidson, P. D. Wentzell, B. Millier, J. E. Jaetao, A. L. Rodriguez, and M. Werner-Washburne (2005). An Automated, Pressure-Driven Sampling Device for Harvesting from Liquid Cultures for Genomic and Biochemical Analyses. Journal of Biochemical Analysis, 65(2).
M. J. Martinez, S. Roy, A. B. Archueletta, P. D. Wentzell, S. A. Anna-Arriola, A. L. Rodriguez, A. D. Aragon, G. A. Quinones, C. Allen, M. Werner-Washburne (2004). Analysis of Stationary Phase and Exit in Saccharomyces cerevisiae: Gene Expression and Identification of Novel Essential Genes. Molecular Biology of the Cell, 15.
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Statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets, that measure different aspects of the cellular state.
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