Rohit Singh

Research Interests

Drug discoveries have been instrumental in improving global health over the last century, but the median drug now takes about 10 years to bring to market and costs over a billion dollars to develop. My lab aims to expedite the development of precise diagnostics and therapeutics by applying machine learning. Our current work is broadly along two directions. Along the first direction, we use single-cell multiomics to discover regulatory mechanisms governing the interaction between the epigenome, transcription factors, and target genes. This approach relies on methodological innovation, developing new Granger causal inference techniques to capitalize on the “parallax” between simultaneous but separate measures of cell state. In the other direction, we apply large language models to model protein interaction and function. These protein language models enable powerful new approaches to predicting and understanding protein-protein and protein-drug interactions. 

Bio

Rohit Singh is an Assistant Professor in the Departments of Biostatistics & Bioinformatics and Cell Biology at Duke Univ. His research interests are broadly in computational biology, with a focus on using machine learning to make drug discovery more efficient. Currently, he's exploring how single-cell genomics and large language models can help decode disease mechanisms and aid in identifying new targets and drugs. He is the recipient of the Test of Time Award at RECOMB, MIT's George M. Sprowls Award for his PhD thesis in Computer Science, and Stanford's Christopher Stephenson Memorial Award for Masters Research in the same field. In addition to academia, he has experience in the industry.

Education

• Ph.D. Massachusetts Institute of Technology, 2012

Positions

• Assistant Professor of Biostatistics & Bioinformatics
• Assistant Professor of Cell Biology
• Assistant Professor of Computer Science
• Assistant Professor in the Department of Electrical and Computer Engineering

Courses Taught

• CELLBIO 493: Research Independent Study

Publications

• Sledzieski S, Versavel C, Singh R, Ocitti F, Devkota K, Kumar L, et al. Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC. 2024.
• Forrester MT, Egol JR, Ozbay S, Singh R, Tata PR. Topology-Driven Discovery of Transmembrane Protein S-Palmitoylation. bioRxiv. 2024 Sep 8;
• Devkota K, Shonai D, Mao J, Soderling S, Singh R. Miniaturizing, Modifying, and Augmenting Nature’s Proteins with Raygun. bioRxiv. 2024.
• Sledzieski S, Devkota K, Singh R, Cowen L, Berger B. TT3D: Leveraging precomputed protein 3D sequence models to predict protein-protein interactions. Bioinformatics. 2023 Nov 1;39(11).
• Ewen-Campen B, Luan H, Xu J, Singh R, Joshi N, Thakkar T, et al. split-intein Gal4 provides intersectional genetic labeling that is repressible by Gal80. Proc Natl Acad Sci U S A. 2023 Jun 13;120(24):e2304730120.
• Singh R, Sledzieski S, Bryson B, Cowen L, Berger B. Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proc Natl Acad Sci U S A. 2023 Jun 13;120(24):e2220778120.
• Ewen-Campen B, Luan H, Xu J, Singh R, Joshi N, Thakkar T, et al. split-intein Gal4 provides intersectional genetic labeling that is fully repressible by Gal80. Cold Spring Harbor Laboratory. 2023.
• Singh R, Im C, Qiu Y, Mackness B, Gupta A, Sorenson T, et al. Learning the Language of Antibody Hypervariability. bioRxiv. 2023.
• Kumar L, Brenner N, Sledzieski S, Olaosebikan M, Roger LM, Lynn-Goin M, et al. Transfer of knowledge from model organisms to evolutionarily distant non-model organisms: The coral Pocillopora damicornis membrane signaling receptome. PLoS One. 2023;18(2):e0270965.
• Wu AP-Y, Singh R, Walsh C, Berger B. Unveiling causal regulatory mechanisms through cell-state parallax. bioRxiv. 2023.
• Ozbay S, Parekh A, Singh R. Navigating the manifold of single-cell gene coexpression to discover interpretable gene programs. bioRxiv. 2023.
• Wu AP, Markovich T, Berger B, Hammerla N, Singh R. Causally-guided Regularization of Graph Attention Improves Generalizability. 2022.
• Singh R, Wu AP, Berger B. Granger causal inference on DAGs identifies genomic loci regulating transcription. 2022.
• Singh R, Devkota K, Sledzieski S, Berger B, Cowen L. Topsy-Turvy: integrating a global view into sequence-based PPI prediction. Bioinformatics. 2022 Jun 24;38(Suppl 1):i264–72.
• Sledzieski S, Singh R, Cowen L, Berger B. Contrasting drugs from decoys. bioRxiv. 2022.
• Singh R, Li JSS, Tattikota SG, Liu Y, Xu J, Hu Y, et al. Prioritizing transcription factor perturbations from single-cell transcriptomics. bioRxiv. 2022.
• Singh R, Wu A, Mudide A, Berger B. Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors. bioRxiv. 2022.
• Sledzieski S, Singh R, Cowen L, Berger B. Adapting protein language models for rapid DTI prediction. bioRxiv. 2022.
• Singh R, Sledzieski S, Cowen L, Berger B. Learning the Drug-Target Interaction Lexicon. bioRxiv. 2022.
• Singh R, Wu AP, Berger B. GRANGER CAUSAL INFERENCE ON DAGS IDENTIFIES GENOMIC LOCI REGULATING TRANSCRIPTION. In: ICLR 2022 - 10th International Conference on Learning Representations. 2022.
• Sledzieski S, Singh R, Cowen L, Berger B. D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions. Cell Syst. 2021 Oct 20;12(10):969-982.e6.
• Singh R, Hie BL, Narayan A, Berger B. Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biol. 2021 May 3;22(1):131.
• Singh R, Berger B. Deciphering the species-level structure of topologically associating domains. bioRxiv. 2021.
• Kumar L, Brenner N, Sledzieski S, Olaosebikan M, Lynn-Goin M, Putnam H, et al. Transfer of Knowledge from Model Organisms to Evolutionarily Distant Non-Model Organisms: The CoralPocillopora damicornisMembrane Signaling Receptome. bioRxiv. 2021.
• Sledzieski S, Singh R, Cowen L, Berger B. Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model. bioRxiv. 2021.
• Singh R, Hie B, Narayan A, Berger B. Metric learning enables synthesis of heterogeneous single-cell modalities. bioRxiv. 2019.
• Friedman AA, Tucker G, Singh R, Yan D, Vinayagam A, Hu Y, et al. Proteomic and functional genomic landscape of receptor tyrosine kinase and ras to extracellular signal-regulated kinase signaling. Sci Signal. 2011 Oct 25;4(196):rs10.
• Hosur R, Singh R, Berger B. Sparse estimation for structural variability. Algorithms Mol Biol. 2011 Apr 19;6:12.
• Park D, Singh R, Baym M, Liao C-S, Berger B. IsoBase: a database of functionally related proteins across PPI networks. Nucleic Acids Res. 2011 Jan;39(Database issue):D295–300.
• Hosur R, Singh R, Berger B. Sparse estimation for structural variability. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010. p. 13–27.
• Singh R, Park D, Xu J, Hosur R, Berger B. Struct2Net: a web service to predict protein-protein interactions using a structure-based approach. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W508–15.
• Kaplow IM, Singh R, Friedman A, Bakal C, Perrimon N, Berger B. RNAiCut: automated detection of significant genes from functional genomic screens. Nat Methods. 2009 Jul;6(7):476–7.
• Liao C-S, Lu K, Baym M, Singh R, Berger B. IsoRankN: spectral methods for global alignment of multiple protein networks. In: Bioinformatics. 2009. p. i253–8.
• Berger B, Singh R, Xu J. Graph algorithms for biological systems analysis. In: Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms. 2008. p. 142–51.
• Singh R, Xu J, Berger B. Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc Natl Acad Sci U S A. 2008 Sep 2;105(35):12763–8.
• Singh R, Xu J, Berger B. Global alignment of multiple protein interaction networks. In: Pac Symp Biocomput. 2008. p. 303–14.
• Sterner B, Singh R, Berger B. Predicting and annotating catalytic residues: an information theoretic approach. J Comput Biol. 2007 Oct;14(8):1058–73.
• Singh R, Xu J, Berger B. Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 16–31.
• Sontag D, Singh R, Berger B. Probabilistic modeling of systematic errors in two-hybrid experiments. In: Pac Symp Biocomput. 2007. p. 445–57.
• Singh R, Xu J, Berger B. Struct2net: integrating structure into protein-protein interaction prediction. In: Pac Symp Biocomput. 2006. p. 403–14.
• Singh R, Palmer N, Gifford D, Berger B, Bar-Joseph Z. Active learning for sampling in time-series experiments with application to gene expression analysis. In: ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning. 2005. p. 833–40.
• Singh R, Bergert B. Chaintweak: sampling from the neighbourhood of a protein conformation. In: Pac Symp Biocomput. 2005. p. 52–63.
• Singh R, Saha M. Identifying structural motifs in proteins. Pac Symp Biocomput. 2003;228–39.