74. Functional and comparative genomics reveals conserved noncoding sequences in the nitrogen‐fixing clade
WJ Pereira, S Knaack, S Chakraborty, D Conde, RA Folk, PM Triozzi, …
New Phytologist 234 (2), 634-649 (2022)
73. GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization
DI Lee, S Roy
Genome biology 22 (1), 1-31 (2021)
72. Evolution of regulatory networks associated with traits under selection in cichlids
TK Mehta, C Koch, W Nash, SA Knaack, P Sudhakar, M Olbei, …
Genome Biology 22 (1), 1-28 (2021)
71. The NIH somatic cell genome editing program<
K Saha, EJ Sontheimer, PJ Brooks, MR Dwinell, CA Gersbach, DR Liu, …
Nature 592 (7853), 195-204 (2021)
70. Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder
K Huang, Y Wu, J Shin, Y Zheng, AF Siahpirani, Y Lin, Z Ni, J Chen, J You, …
PLoS genetics 17 (2), e1009309 (2021)
69. A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies
J Shin, H Marx, A Richards, D Vaneechoutte, D Jayaraman, J Maeda, …
Nucleic Acids Research 49 (1), e3-e3 (2021)
68. Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula
SA Knaack, D Conde, KM Balmant, TB Irving, LG Maia, PM Triozzi, …
bioRxiv (2021)
67. Modular Derivation and Unbiased Single-cell Analysis of Regional Human Hindbrain And Spinal Neurons Enables Discovery of Nuanced Transcriptomic Patterns along Developmental Axes
NR Iyer, J Shin, S Cuskey, Y Tian, NR Nichol, TE Doersch, SG McCalla, …
bioRxiv (2021)
66. Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation
BA Baur, J Shin, J Schreiber, S Zhang, Y Zhang, M Manjunath, JS Song, …
bioRxiv (2021)
65. Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data
M Stone, SG McCalla, AF Siahpirani, V Periyasamy, J Shin, S Roy
bioRxiv (2021)
64. Dynamic regulatory module networks for inference of cell type-specific transcriptional networks
AF Siahpirani, S Knaack, D Chasman, M Seirup, R Sridharan, R Stewart, …
bioRxiv, 2020.07. 18.210328 (2021)
63. Data integration for inferring context-specific gene regulatory networks
B Baur, J Shin, S Zhang, S Roy
Current opinion in systems biology 23, 38-46 (2020)
62. Identification of FMR1-regulated molecular networks in human neurodevelopment
M Li, J Shin, RD Risgaard, MJ Parries, J Wang, D Chasman, S Liu, S Roy, …
Genome Research 30 (3), 361-374 (2020)
61. Chromatin Accessibility Characterization of the Gene Regulatory Network Controlling Nodulation Factors Response in Medicago truncatula
D Conde, S Knaack, K Balmant, L Maia, T Irving, C Dervinis, M Crook, …
Plant and Animal Genome XXVIII Conference (January 11-15, 2020)
60. Simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization
DI Lee, S Roy
bioRxiv (2020)
59. ABC-GWAS: Functional Annotation of Estrogen Receptor-Positive Breast Cancer Genetic Variants
M Manjunath, Y Zhang, S Zhang, S Roy, P Perez-Pinera, JS Song
Frontiers in genetics, 730 (2020)
58. In silico prediction of high-resolution Hi-C interaction matrices
S Zhang, D Chasman, S Knaack, S Roy
Nature Communications 10 (1), 1-18 (2019)
57. 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 (2019)
56. 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 (2019)
55. 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, 754 (2019)
54. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks
AF Siahpirani, D Chasman, S Roy
Gene Regulatory Networks, 161-194 (2019)
53. 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 (2018)
52. 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 (2018)
51. 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 (2018)
50. 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 (2017)
49. 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 (2017)
48. 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 (2017)
47. A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions
A Fotuhi Siahpirani, F Ay, S Roy
Genome biology 17 (1), 1-18 (2016)
46. A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium melilotiH Marx, CE Minogue, D Jayaraman, AL Richards, NW Kwiecien, …
Nature biotechnology 34 (11), 1198-1205 (2016)
45. A prior-based integrative framework for functional transcriptional regulatory network inference
AF Siahpirani, S Roy
Nucleic acids research 45 (4), e21-e21 (2016)
44. 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 (2016)
43. Network-based approaches for analysis of complex biological systems
D Chasman, AF Siahpirani, S Roy
Current opinion in biotechnology 39, 157-166 (2016)
42. 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 (2016)
41. 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 (2016)
40. Reconstruction and analysis of the evolution of modular transcriptional regulatory programs using Arboretum
SA Knaack, DA Thompson, S Roy
Yeast Functional Genomics, 375-389 (2016)
39. 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 (2015)
38. 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 (2015)
37. Comparative analysis of gene regulatory networks: from network reconstruction to evolution
D Thompson, A Regev, S Roy
Annu Rev Cell Dev Biol 31 (1), 399-428 (2015)
36. 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 (2015)
35. 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 (2015)
34. 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 (2015)
33. 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 (2014)
32. 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 (2014)
31. 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), e1003252 (2013)
30. Correction: Evolutionary principles of modular gene regulation in yeasts
DA Thompson, S Roy, M Chan, MP Styczynski, J Pfiffner, C French, …
Elife 2, e01114 (2013)
29. Evolutionary principles of modular gene regulation in yeasts
DA Thompson, S Roy, M Chan, MP Styczynsky, J Pfiffner, C French, …
Elife 2, e00603 (2013)
28. 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 (2013)
27. 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 (2013)
26. A graph-based comparative analysis of three-dimensional organization of chromosomes in yeast and mammals.
S Roy, R Atlas (2012)
25. 6.047/6.878 Lecture 18 Regulatory Networks: Inference, Analysis, Application
S Roy, S Feizi (2012)
24. 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 (2012)
23. 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 (2012)
22. Comparative functional genomics of the fission yeasts
N Rhind, Z Chen, M Yassour, DA Thompson, BJ Haas, N Habib, …
Science 332 (6032), 930-936 (2011)
21. A multiple network learning approach to capture system-wide condition-specific responses
S Roy, M Werner-Washburne, T LaneBioinformatics 27 (13), 1832-1838 (2011)
20. The proteomics of quiescent and nonquiescent cell differentiation in yeast stationary-phase culturesGS Davidson, RM Joe, S Roy, O Meirelles, CP Allen, MR Wilson, …
Molecular biology of the cell 22 (7), 988-998 (2011)
19. 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 (2011)
18. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE
modENCODE Consortium, S Roy, J Ernst, PV Kharchenko, P Kheradpour, …
Science 330 (6012), 1787-1797 (2010)
17. Information-Theoretic Inference of Gene Networks Using Backward Elimination.
P Meyer, D Marbach, S Roy, M Kellis
BioComp, 700-705 (2010)
16. Exploiting amino acid composition for predicting protein-protein interactions
S Roy, D Martinez, H Platero, T Lane, M Werner-Washburne
PloS one 4 (11), e7813 (2009)
15. Scalable learning of large networks
S Roy, S Plis, M Werner-Washburne, T Lane
IET systems biology 3 (5), 404-413 (2009)
14. Learning structurally consistent undirected probabilistic graphical models
S Roy, T Lane, M Werner-Washburne
Proceedings of the 26th annual international conference on machine learning … (2009)
13. 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 (2009)
12. Learning condition-specific networks
S Roy
The University of New Mexico (2009)
11. A system for generating transcription regulatory networks with combinatorial control of transcription
S Roy, M Werner-Washburne, T LaneBioinformatics 24 (10), 1318-1320 (2008)
10. Characterization of differentiated quiescent and nonquiescent cells in yeast stationary-phase culture
AD Aragon, AL Rodriguez, O Meirelles, S Roy, GS Davidson, PH Tapia, …
Molecular biology of the cell 19 (3), 1271-1280 (2008)
9. Reliable prediction of regulator targets using 12 Drosophila genomes
P Kheradpour, A Stark, S Roy, M Kellis
Genome research 17 (12), 1919-1931 (2007)
8. 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 (2007)
7. Integrative Construction and Analysis of Condition-specific Biological Networks.
S Roy, T Lane, M Werner-Washburne
AAAI, 1898-1899 (2007)
6. 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 (2007)
5. Multivariate curve resolution of time course microarray data
PD Wentzell, TK Karakach, S Roy, MJ Martinez, CP Allen, …
BMC bioinformatics 7 (1), 1-19 (2006)
4. 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 (2006)
3. 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), 1-13 (2006)
2. 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 (2005)
1. A genomic analysis of quiescence and exit from the quiescent state in S-cerevisiae.
J Martinez, A Aragon, A Archuletta, A Rodriguez, S Roy, …
Yeast 20, S343-S343 (2003)
TECHNICAL REPORTS
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.
POSTERS AND WORKSHOPS
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.
DISSERTATION
Learning condition-specific networks (2009). UNM PhD Dissertation.
MASTER’S THESIS
A Machine Learning Approach for Information Extraction (2005). UNM Master’s thesis.
