Ramana V Davuluri

About

Ramana V Davuluri is a Professor in the Department of Biomedical Informatics at Stony Brook University. His research focuses on Machine Learning applications in Cancer Data Science and Gene Regulation. He is involved in advancing computational methods to better understand biological data, particularly in the context of cancer research and genomics. His work contributes to the development of data-driven approaches for understanding complex biological systems and improving disease diagnosis and treatment.

Research topics

• Biology
• Genetics
• Cancer research
• Computational biology
• Medicine

Selected publications

• DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

  Bioinformatics · 2021-02-03 · 1220 citations

  articleOpen accessSenior authorCorresponding

  MOTIVATION: Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. RESULTS: To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA se…

  PublisherOA PDFDOI

• A first-in-human phase 0 clinical study of RNA interference–based spherical nucleic acids in patients with recurrent glioblastoma

  Science Translational Medicine · 2021-03-10 · 314 citations

  articleOpen access

  Glioblastoma (GBM) is one of the most difficult cancers to effectively treat, in part because of the lack of precision therapies and limited therapeutic access to intracranial tumor sites due to the presence of the blood-brain and blood-tumor barriers. We have developed a precision medicine approach for GBM treatment that involves the use of brain-penetrant RNA interference-based spherical nucleic acids (SNAs), which consist of gold nanoparticle cores covalently conjugated with radially oriented…

  PublisherOA PDFDOI

• DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

  arXiv (Cornell University) · 2023-06-26 · 161 citations

  preprintOpen access

  Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area. Existing works have largely hinged on k-mer, fixed-length permutations of A, T, C, and G, as the token of the genome language due to its simplicity. However, we argue that the computation and sample inefficiencies introduced by k-mer tokenization are primary obstacles in developing large genom…

  PublisherOA PDFDOI

• A community effort to optimize sequence-based deep learning models of gene regulation

  Nature Biotechnology · 2024-10-11 · 29 citations

  articleOpen access

  A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural network…

  PublisherOA PDFDOI

• DNABERT-S: pioneering species differentiation with species-aware DNA embeddings

  Bioinformatics · 2025-07-01 · 21 citations

  articleOpen access

  SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trai…

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01CA135444

  NIH · $651k · 2012

• NIH Grant R01HG003362

  NIH · $1.3M · 2011

• Informatics Platform for Mammalian Gene Regulation at Isoform-level

  NIH · $2.4M · 2013–2022

Frequent coauthors

• Yingtao Bi

  AbbVie (United States)

  62 shared

• Hao Sun

  61 shared

• Sandya Liyanarachchi

  57 shared

• Milena S. Nicoloso

  Centro di Riferimento Oncologico

  56 shared

• Lianchun Xiao

  The University of Texas MD Anderson Cancer Center

  51 shared

• Christoph Plass

  Epigenomics (Germany)

  49 shared

• Carlo M. Croce

  The Ohio State University

  45 shared

• George A. Calin

  The University of Texas MD Anderson Cancer Center

  45 shared

Education

• M.S.

  Indian Agricultural Statistics Research Institute

  2001

• Ph.D.

  Indian Agricultural Statistics Research Institute

  1996

Similar researchers at Stony Brook University

• Michael R. Douglash-91
• Miriam Rafailovichh-84
• Joseph S.B. Mitchellh-65