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Staff Profile: Rajarshi Guha

Rajarshi Guha, Ph.D.
Rajarshi Guha, Ph.D.

Research Scientist, Informatics (Contractor)

Division of Pre-Clinical Innovation

National Center for Advancing Translational Sciences

National Institutes of Health

E-mail Rajarshi Guha

Biography

Rajarshi Guha is an informatics research scientist in the Division of Pre-Clinical Innovation at NCATS. He provides informatics support and development for multiple research platforms, including small-molecule, RNA interference and combination screening. In addition to individual project support, he is involved in software and algorithm development in the areas of cheminformatics methods and chemical biology databases, including the BioAssay Research Database. Before joining NIH, Guha was a visiting assistant professor in the School of Informatics at Indiana University, where he currently holds an adjunct professorship. An active member of the cheminformatics community, he has made contributions to various open-source projects and has held multiple leadership roles in the American Chemical Society’s Division of Chemical Information. Guha earned his Ph.D. in computational chemistry at Pennsylvania State University and held postdoctoral research positions at Penn State and Indiana University.

Research Topics

Guha’s research interests focus on methodology development to analyze and visualize chemical biology data sets, with specific focus on the development of techniques that can explain the effects of small molecules in the context of larger biological systems using heterogeneous data types and modern network methods. His recent work has focused on the development of novel analytic and visualization methods for combination screening results.

Selected Publications

  1. RNA Polymerase II Regulates Topoisomerase 1 Activity to Favor Efficient Transcription.
  2. High-throughput matrix screening identifies synergistic and antagonistic antimalarial drug combinations.
  3. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives.
  4. On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?
  5. Transcriptomic profiling and quantitative high-throughput (qHTS) drug screening of CDH1 deficient hereditary diffuse gastric cancer (HDGC) cells identify treatment leads for familial gastric cancer.