Office 
(631) 638-2590

Email 
Ramana.Davuluri@stonybrookmedicine.edu

Stony Brook University
Department of Biomedical Informatics
100 Nicolls Road
Stony Brook, NY 11794

Research Program

Imaging, Biomarker Discovery and Engineering Sciences

Department

Department of Biomedical Informatics

Research Interest

Dr. Davuluri’s research focuses on the development of machine learning algorithms and informatics solutions for problems in isoform-level gene regulation and precision medicine. Using state-of-art machine learning and statistical methods, Davuluri group has been developing predictive algorithms for TF binding sites, Pol-II promoters, and transcriptional modules from ChIP-seq/chip data, and Isoform level gene expression estimation and exon-level differential expression analysis from exon-array and NGS data. 

His group has developed PIGExClass (platform-independent isoform-level gene-expression based classification-system) data-mining framework, a robust computational approach to derive and then transfer gene-signatures from one analytical platform to another for designing clinically adaptable molecular subtyping assays. PIGExClass was successfully applied on glioblastoma and high-grade ovarian cancer for developing robust platform-independent molecular profiling assays for cancer patient stratification. The classifiers derived by PIGExClass have the potential to develop into prognostic biomarkers for stratification of cancer patients. 

Davuluri's group has developed an informatics pipeline to evaluate whether some protein isoforms (resulting from alternative splicing) has the potential to serve as therapeutic targets in cancer, which is a missing piece in majority of drug discovery processes. By integrating information from publicly available databases, his group has curated FDA approved or investigational stage small molecule cancer drugs that target different genes. By analyzing the interactions with binding pocket information, his group has found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. His group is investigating isoform-level drug-target interactions that could play important role in on- and off-target effects at splice-variant-level to enhance the productivity of drug-discovery research.

Davuluri's group has recently developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. The significance of his work is the creation of a pre-trained DNABERT model and fine-tuning modules for specific sequence prediction tasks, based on a new breed of deep-learning algorithms. These algorithms optimize the specific sequence prediction tasks and interpretation of germline and somatic sequence variants in non-coding genomic regions that play important role in cancer initiation and progression. His ongoing research is focused on developing novel computational tools for predicting polysemous cis-regulatory elements and splice sites that are disrupted in cancer genomes, which will likely lead to the identification of non-coding genetic variants that offer crucial applications in cancer biomarker discovery. 

Education

BS, Mathematics, Nagarjuna University, India, 1988
MS, Statistics and Computer Application, Indian Agricultural Statistics Research Institute (IARI), New Delhi,India, 1991
PHD, Statistics and Computer Application, Indian Agricultural Statistics Research Institute (IARI), New Delhi,India, 1996
Post Doc Fellow, Bioinformatics, University of Ghent, Gent, Belgium, 1998
Post Doc Fellow, Computational Biology, Cold Spring Harbor Laboratory (CSHL), Cold Spring, NY, 2001

Publications

A complete list of publications can be found HERE.