In silico prediction of high-resolution Hi-C interaction matrices
S Zhang, D Chasman, S Knaack, S Roy
Nature Communications 10 (1), 1-18

Imputed gene associations identify replicable trans‐acting genes enriched in transcription pathways and complex traits
HE Wheeler, S Ploch, AN Barbeira, R Bonazzola, A Andaleon, …
Genetic epidemiology 43 (6), 596-608

Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency
KA Tran, SJ Pietrzak, NZ Zaidan, AF Siahpirani, SG McCalla, AS Zhou, …
Cell reports 27 (6), 1726-1741. e5

The cancer-associated genetic variant rs3903072 modulates immune cells in the tumor microenvironment
Y Zhang, M Manjunath, J Yan, BA Baur, S Zhang, S Roy, JS Song
Frontiers in Genetics 10, 754

Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks
AF Siahpirani, D Chasman, S Roy
Gene Regulatory Networks, 161-194

Integrative genomic analysis discovers the causative regulatory mechanisms of a breast cancer-associated genetic variant
Y Zhang, M Manjunath, S Zhang, D Chasman, S Roy, JS Song
Cancer Research 78 (13 Supplement), 1220-1220

Integrative genomic analysis predicts causative cis-regulatory mechanisms of the breast cancer–associated genetic variant rs4415084
Y Zhang, M Manjunath, S Zhang, D Chasman, S Roy, JS Song
Cancer research 78 (7), 1579-1591

Can cancer GWAS variants modulate immune cells in the tumor microenvironment?
Y Zhang, M Manjunath, J Yan, BA Baur, S Zhang, S Roy, JS Song
bioRxiv, 493171

Evolution of regulatory networks controlling adaptive traits in cichlids
TK Mehta, C Koch, W Nash, SA Knaack, P Sudhakar, M Olbei, …
BioRxiv, 496034

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes
S Roy, R Sridharan
Genome research 27 (7), 1250-1262

Inference and evolutionary analysis of genome-scale regulatory networks in large phylogenies
C Koch, J Konieczka, T Delorey, A Lyons, A Socha, K Davis, SA Knaack, …
Cell systems 4 (5), 543-558. e8

Physiological responses and gene co-expression network of mycorrhizal roots under K+ deprivation
K Garcia, D Chasman, S Roy, JM Ané
Plant physiology 173 (3), 1811-1823

A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions
AF Siahpirani, F Ay, S Roy
Genome biology 17 (1), 114

A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti
H Marx, CE Minogue, D Jayaraman, AL Richards, NW Kwiecien, …
Nature biotechnology 34 (11), 1198

A prior-based integrative framework for functional transcriptional regulatory network inference
AF Siahpirani, S Roy
Nucleic acids research 45 (4), e21-e21

Integrating transcriptomic and proteomic data using predictive regulatory network models of host response to pathogens
D Chasman, KB Walters, TJS Lopes, AJ Eisfeld, Y Kawaoka, S Roy
PLoS computational biology 12 (7), e1005013

Network-based approaches for analysis of complex biological systems
D Chasman, AF Siahpirani, S Roy
Current opinion in biotechnology 39, 157-166

A predictive modeling approach for cell line-specific long-range regulatory interactions
S Roy, AF Siahpirani, D Chasman, S Knaack, F Ay, R Stewart, M Wilson, …
Nucleic acids research 44 (4), 1977

Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions
Z Niu, D Chasman, AJ Eisfeld, Y Kawaoka, S Roy
Bioinformatics 32 (10), 1509-1517

Reconstruction and analysis of the evolution of modular transcriptional regulatory programs using Arboretum
SA Knaack, DA Thompson, S Roy
Yeast Functional Genomics, 375-389

Comparative analysis of gene regulatory networks: from network reconstruction to evolution
D Thompson, A Regev, S Roy
Annual review of cell and developmental biology 31, 399-428

A predictive modeling approach for cell line-specific long-range regulatory interactions
S Roy, AF Siahpirani, D Chasman, S Knaack, F Ay, R Stewart, M Wilson, …
Nucleic acids research 43 (18), 8694-8712

Deep sequencing of the Medicago truncatula root transcriptome reveals a massive and early interaction between nodulation factor and ethylene signals
E Larrainzar, BK Riely, SC Kim, N Carrasquilla-Garcia, HJ Yu, HJ Hwang, …
Plant Physiology 169 (1), 233-265

SIRT3 mediates multi-tissue coupling for metabolic fuel switching
KE Dittenhafer-Reed, AL Richards, J Fan, MJ Smallegan, AF Siahpirani, …
Cell metabolism 21 (4), 637-646

Collaborative rewiring of the pluripotency network by chromatin and signalling modulating pathways
KA Tran, SA Jackson, ZPG Olufs, NZ Zaidan, N Leng, C Kendziorski, …
Nature communications 6 (1), 1-14

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia
SM Plis, J Sui, T Lane, S Roy, VP Clark, VK Potluru, RJ Huster, A Michael, …
Neuroimage 102, 35-48

A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components
SA Knaack, AF Siahpirani, S Roy
Cancer informatics 13, CIN. S14058

Integrated module and gene-specific regulatory inference implicates upstream signaling networks
S Roy, S Lagree, Z Hou, JA Thomson, R Stewart, AP Gasch
PLoS computational biology 9 (10)

Correction: Evolutionary principles of modular gene regulation in yeasts
DA Thompson, S Roy, M Chan, MP Styczynski, J Pfiffner, C French, …
Elife 2, e01114

Evolutionary principles of modular gene regulation in yeasts
DA Thompson, S Roy, M Chan, MP Styczynsky, J Pfiffner, C French, …
Elife 2, e00603

Arboretum: reconstruction and analysis of the evolutionary history of condition-specific transcriptional modules
S Roy, I Wapinski, J Pfiffner, C French, A Socha, J Konieczka, N Habib, …
Genome research 23 (6), 1039-1050

Calorie restriction and SIRT3 trigger global reprogramming of the mitochondrial protein acetylome
AS Hebert, KE Dittenhafer-Reed, W Yu, DJ Bailey, ES Selen, …
Molecular cell 49 (1), 186-199

A graph-based comparative analysis of three-dimensional organization of chromosomes in yeast and mammals.
S Roy, R Atlas

6.047/6.878 Lecture 18 Regulatory Networks: Inference, Analysis, Application
S Roy, S Feizi

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks
D Marbach, S Roy, F Ay, PE Meyer, R Candeias, T Kahveci, CA Bristow, …
Genome research 22 (7), 1334-1349

Predictive Regulatory Models in of Transcriptional Networks
D Marbach, S Roy, F Ay, PE Meyer, R Candeias, T Kahveci, CA Bristow, …
Cold Spring Harbor Laboratory Press

Comparative functional genomics of the fission yeasts
N Rhind, Z Chen, M Yassour, DA Thompson, BJ Haas, N Habib, …
Science 332 (6032), 930-936

A multiple network learning approach to capture system-wide condition-specific responses
S Roy, M Werner-Washburne, T Lane
Bioinformatics 27 (13), 1832-1838

The proteomics of quiescent and nonquiescent cell differentiation in yeast stationary-phase cultures
GS Davidson, RM Joe, S Roy, O Meirelles, CP Allen, MR Wilson, …
Molecular biology of the cell 22 (7), 988-998

Aging and the survival of quiescent and non-quiescent cells in yeast stationary-phase cultures
M Werner-Washburne, S Roy, GS Davidson
Aging research in yeast, 123-143

Identification of functional elements and regulatory circuits by Drosophila modENCODE
S Roy, J Ernst, PV Kharchenko, P Kheradpour, N Negre, ML Eaton, …
Science 330 (6012), 1787-1797

Information-Theoretic Inference of Gene Networks Using Backward Elimination.
P Meyer, D Marbach, S Roy, M Kellis
BioComp, 700-705

Scalable learning of large networks
S Roy, S Plis, M Werner-Washburne, T Lane
IET systems biology 3 (5), 404-413

Learning structurally consistent undirected probabilistic graphical models
S Roy, T Lane, M Werner-Washburne
Proceedings of the 26th annual international conference on machine learning …

Exploiting amino acid composition for predicting protein-protein interactions
S Roy, D Martinez, H Platero, T Lane, M Werner-Washburne
PloS one 4 (11)

Inference of functional networks of condition-specific response-a case study of quiescence in yeast
S Roy, T Lane, M Werner-Washburne, D Martinez
Biocomputing 2009, 51-62

Learning condition-specific networks
S Roy

A system for generating transcription regulatory networks with combinatorial control of transcription
S Roy, M Werner-Washburne, T Lane
Bioinformatics 24 (10), 1318-1320

Characterization of differentiated quiescent and nonquiescent cells in yeast stationary-phase cultures
AD Aragon, AL Rodriguez, O Meirelles, S Roy, GS Davidson, PH Tapia, …
Molecular biology of the cell 19 (3), 1271-1280

Reliable prediction of regulator targets using 12 Drosophila genomes
P Kheradpour, A Stark, S Roy, M Kellis
Genome research 17 (12), 1919-1931

Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures
A Stark, MF Lin, P Kheradpour, JS Pedersen, L Parts, JW Carlson, …
Nature 450 (7167), 219

Integrative Construction and Analysis of Condition-specific Biological Networks.
S Roy, T Lane, M Werner-Washburne
Proceedings of the National Conference on Artificial Intelligence 22 (2), 1898

A simulation framework for modeling combinatorial control in transcription regulatory networks
S Roy, T Lane, M Werner-Washburne
UNM Computer Science Technical Report, TR-CS-2007-06, 1-10

Multivariate curve resolution of time course microarray data
PD Wentzell, TK Karakach, S Roy, MJ Martinez, CP Allen, …
BMC bioinformatics 7 (1), 343

A hidden-state Markov model for cell population deconvolution
S Roy, T Lane, C Allen, AD Aragon, M Werner-Washburne
Journal of Computational Biology 13 (10), 1749-1774

Release of extraction-resistant mRNA in stationary phase Saccharomyces cerevisiae produces a massive increase in transcript abundance in response to stress
AD Aragon, GA Quiñones, EV Thomas, S Roy, M Werner-Washburne
Genome biology 7 (2), R9

A datamining approach to cell population deconvolution from gene expressions using particle filters
S Roy, T Lane, M Werner-Washburne
Proceedings of the 5th international workshop on Bioinformatics, 46-53

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.

Reconstruction and analysis of evolutionary history of condition-specific transcriptional programs of multiple species. Talk at Cold Spring Harbour Laboratory meeting on Genome Informatics, 2011.

Re-constructing the structural and functional components of genome-wide regulatory networks. Talk at Cold Spring Harbour Laboratory meeting on Systems Biology: Networks meeting, 2011.

Inferring predictive regulatory networks in Drosophila melanogaster by large-scale data integration. Poster presentation at the CSHL Biology of Genomes meeting, 2011.

S. Roy, C. A. Bristow, J. Konieczka, P. Kheradpour, A. Regev, M. Kellis. A Mixture of Experts model for predicting expression from sequence (2010). Intelligent Systems in Molecular Biology.

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.