Dissertation

Learning condition-specific networks (2009). UNM PhD Dissertation.

Master's thesis

A Machine Learning Approach for Information Extraction (2005). UNM Master's thesis.

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-speciﬁc 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.

S. Roy, T. Lane, M. Werner-Washburne (2009). Learning Probabilistic Networks of Condition-Specific Response: Digging Deep in Yeast Stationary Phase. UNM Computer Science Technical Report, TR-CS-2009-07.

S. Roy, T. Lane, M. Werner-Washburne (2008). Learning structurally consistent undirected probabilistic graphical models. UNM Computer Science Technical Report, TR-CS-2008-14.

S. Roy, T. Lane, M. Werner-Washburne (2007). A Simulation Framework for Modeling Combinatorial Control in Transcription Regulatory Networks. UNM Computer Science Technical Report, TR-CS-2007-06.

S. Roy, T. Lane, C. Allen, A. D. Aragon, M. Werner-Washburne (2004). A Sequential Monte Carlo Sampling Approach for Cell Population Deconvolution from Microarray Data.

S. Roy, T. Lane, M. Werner-Washburne (2009). Learning condition-specific networks. Third Annual q-bio Conference on Cellular Information Processing. Santa Fe. New Mexico, USA.

S. Roy, S. Plis, M. Werner-Washburne (2008). Scalable learning of large networks. Second Annual q-bio Conference on Cellular Information Processing. Santa Fe. New Mexico, USA.

S. Roy, A. Stark, P. Kheradpour, M. Kellis, M. Werner-Washburne, T. Lane (2008). A relational framework for predicting tissues and links in the Drosophila regulatory network. Poster at RECOMB Satellite on Regulatory Genom ics and Systems Biology.

S. Roy, T. Lane, M. Werner-Washburne (2008). Integrative Construction and Analysis of Condition-specific Biological Network. Thirteenth AAAI Doctoral Consortium. Chicago. Illinois, USA

S. Roy, T. Lane, M. Werner-Washburne (2007). Intergative construction and analysis of condition-specific biological networks. AAAI Student Abstract and Poster Program. Vancouver, Canada.

S. Roy, T. Lane, M. Werner-Washburne (2006). Predicting protein-protein interactions using amino-acid composition. Second Annual RECOMB Satellite Workshop on Systems Biology. S.Roy, T. Lane, C. Allen, A. D. Aragon, M. Werner-Washburne (2006). Cell population deconvolution using particle filter. Poster presentation at the Tenth Annual International Conference on Research in Computational Molecular Biology (RECOMB).

S. Roy, T. Lane, C. Allen, A. D. Aragon, M. Werner-Washburne (2005). A Datamining approach to cell population deconvolution from gene expressions using particle filters. Fifth ACM SIGKDD Workshop on Data Mining in Bioinformatics.