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MSCTR | Cancer Research |Translational Research

Integrated Head and Neck Oncology Program

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Stem Cell Program

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Molecular Immunology

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Neuro-oncology Program

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Gynaec-oncology Program

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Product Research

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Computational Biology

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Advanced Diagnostic Research Center

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Cancer Outreach Program

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Technology Business Incubator

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Phone +91 80 7122 2351
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Email msmf@ms-mf.org
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Address

NH Health City Bangalore

Dr. Sujan K Dhar, Ph.D.

Dr. Sujan K Dhar, Ph.D.

Computational Biology

Dr. Sujan K Dhar (PhD in Theoretical Physics from Indian Institute of Science) heads the newly formed computation Biology program at MSCTR, MSMF. Following his passion in computation, Dr Dhar started his career as a scientist in the Supercomputer Education and Research Center (SERC) at IISc before joining the software and then the pharma-support Industry. As an entrepreneur in the pharma-support Induatry, he started working with the clinicians and got back to his initial passion of computation, now with a clinically relevant translational focus.

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Working Hours

  • Monday 8AM-7PM
  • Tuesday 8AM-7PM
  • Wednesday 8AM-7PM
  • Thursday 8AM-7PM
  • Saturday 8AM-7PM

Research interests:

Computational Biology, Integration of multi-omic data for understanding of disease biology, Use of Artificial Intellegence and Machine Learning techniques to identfy clinical and molecular subtypes from medical images including pathology and radiology.

Publications

  • Proteogenomic Integration for discovery of differntial biomarkers in Glioblastoma: Glioblastoma is an aggressive form of brain cancer with median survival less than 18 months. In this project, we are analyzing transcriptomics and proteomics data generated from a set of glioblastoma and lower-grade glioma patients using a combination of de novo assembly and alignment-based pipelines to arrive at novel peptides differentially expressed in glioblastoma and other related clinical phenotypes. Dr Ravi Sirdeshmukh, Dr Abhishek Kumar, Institute of Bioinformatics.
  • AI-enabled computational model for IDH1 mutation detection from H&E-stained glioma histopathology images : Mutations in the IDH gene are known to indicate better prognosis for patients suffering from lower-grade glioma. Current pathology practice of detecting the mutation using immunohistochemistry can only detect the canonical mutations. In this project, we aim to develop an artificial neural network-based computational model that can detect both canonical and non-canonical mutations from histopathology images that are generated as part of the standard of care. Dr Akhila L, Pathology, NH
  • Determination of COVID-19 severity indicators from the systematic analysis of public data: Through successive waves of COVID-19, it is now well-established that around 5-10% of infected patients progress to severe form of the disease and other factors like age and pre-existing illnesses increases the chance of severity. In this study, we have analyzed a plethora of publicly available data to establish a set of cytokine markers like IL-6 and IL-10 that be used as a prognostic indicators. We also showed that inflammation and hypercoagulopathy are the pre-dominant risk factors of severity for SARS-CoV-2 infected patients suffering from diabetes.