Shilpa Kobren, PhD
Research Fellow in Biomedical Informatics, Harvard Medical School
Biomedical Informatics Research Training Fellow, 2018-2019
Mentor: Zak Kohane, MD, PhD
Shilpa Nadimpalli Kobren earned her PhD in Computer Science from Princeton University, where she worked under the supervision of Dr. Mona Singh. Kobren's doctoral research focused on developing novel computational methods to detect and interpret protein interaction and cellular network perturbations across species, across healthy human individuals, and across individuals with disease. Kobren's current research leverages genome-scale sequencing and molecular data in the context of heterogeneous clinical data and electronic health record data to derive insights on the molecular mechanisms underlying rare, undiagnosed human diseases.
The contribution of mosaicism to genetic diseases and de novo pathogenic variants.
Authors: Tinker RJ, Bastarache L, Ezell K, Kobren SN, Esteves C, Rosenfeld JA, Macnamara EF, Hamid R, Cogan JD, Rinker D, Mukharjee S, Glass I, Dipple K, Phillips JA.
Am J Med Genet A
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Am J Med Genet A
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Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases.
Authors: Kobren SN, Baldridge D, Velinder M, Krier JB, LeBlanc K, Esteves C, Pusey BN, Züchner S, Blue E, Lee H, Huang A, Bastarache L, Bican A, Cogan J, Marwaha S, Alkelai A, Murdock DR, Liu P, Wegner DJ, Paul AJ, Sunyaev SR, Kohane IS.
Genet Med
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Genet Med
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Innovative methodological approaches for data integration to derive patterns across diverse, large-scale biomedical datasets.
Authors: Beaulieu-Jones B, Darabos C, Kim D, Verma A, Kobren SN.
Pac Symp Biocomput
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Pac Symp Biocomput
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PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.
Ongoing challenges and innovative approaches for recognizing patterns across large-scale, integrative biomedical datasets.
Authors: Kobren SN, Beaulieu-Jones B, Darabos C, Kim D, Verma A.
Pac Symp Biocomput
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Pac Symp Biocomput
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Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions