About Tanmoy
My research leverages machine learning and computational biology to solve critical questions in clinical immunogenetics and disease biology. Focus areas include classifying cross-reactive antibody epitopes from clinical sera, dissecting single-cell immune landscapes, and screening small molecules against oncogenic targets. With an academic background beginning with a Bachelor's and Master's in Computer Science from Vidyasagar University, Medinipur, West Bengal, India, and progressing through the Bose Institute and ICMR–National Institute for Research in Bacterial Infections to postdoctoral research across the University of California, San Diego (UCSD), the Salk Institute, and the University of California, San Francisco (UCSF), I bring deep cross-disciplinary expertise to computational medicine. Additionally, I contribute to the peer-review process for journals spanning applied optimization, remote sensing, and biomedical sciences.
Google Scholar & ResearchGate, Aug 2026
Updates
- Dec 2025Completed Advanced Certifications in AI, Data Science & Research Ethics at MIT, covering responsible AI and human-subjects research ethics.
- Dec 2024Joined the Immunogenetics & Transplantation Laboratory at UCSF under Dr. Rajalingam Raja, building a machine-learning platform for HLA cross-reactive epitope group classification.
- 2023Presented a poster on immune cell network inference in sarcoidosis at the UCSF Pulmonary Retreat.
- May 2023Completed single-cell RNA-seq analysis training at Columbia University.
- OngoingReviewing manuscripts for PLOS ONE, JOTA, the Journal of the Indian Society of Remote Sensing, and Frontiers journals.
What I work on
AI & ML for transplantation and computational biology
Applying artificial intelligence and machine learning models to complex biological, transplant-immunology, and computational biology problems — turning high-dimensional clinical and molecular data into models that inform real decisions in the clinic and the lab.
Immunogenomics & HLA analytics
A clinical platform classifying cross-reactive epitope groups (CREGs) from HLA antibody binding patterns in patient sera, to improve donor–recipient compatibility, graft survival, and reduce sensitization and rejection risk.
AI & machine learning for biomedical data
Predictive and prognostic models from EHR and multi-omics data; protein–protein interaction and signaling-pathway analysis; single-cell approaches to immune cell states and cell–cell communication.
Multimodal AI & vision–language models
Multimodal LLMs integrating radiology imaging with clinical narratives (e.g. MIMIC datasets) to support clinical decision-making and outcome prediction.
Gene expression & single-cell modeling
Deep learning-based comparison of disease versus healthy transcriptomes for immune and non-immune cell classification and functional state prediction.
AI-driven drug discovery
Pharmacophore modeling, docking, and virtual screening of small-molecule libraries for oncogenic PPI target identification and lead optimization.
ToolsSeurat · Scanpy · Muon · CellTypist · AutoDock Vina · PyMOL · Chimera · Python · R · SQL · Git · HPC
Positions
- 2024 — presentPostdoctoral ResearcherUniversity of California, San Francisco — Dept. of SurgeryAdvisor: Rajalingam Raja, PhD
- 2022 — 2024Postdoctoral ResearcherUniversity of California, San FranciscoAdvisors: Prof. Laura Koth · Prof. Mark Ansel
- Sep – Dec 2022Visiting Mentored ResearcherSalk Institute for Biological Studies, San DiegoAdvisor: Prof. Uri Manor
- 2021 — 2022Postdoctoral ResearcherUniversity of California, San DiegoAdvisor: Prof. Rodney Gabriel
- 2020 — 2021Postdoctoral Research AssociateICMR–National Institute for Research in Bacterial Infections, KolkataAdvisor: Dr. Santasabuj Das, Scientist G & Director
- 2015 — 2019Ph.D. Graduate Researcher — BioinformaticsMaulana Abul Kalam Azad Univ. of Technology; research at Bose Institute, KolkataAdvisors: Prof. Sudipto Saha · Prof. Raja Banerjee
- 2012 — 2015Research AssistantBose Institute, KolkataAdvisor: Prof. Sudipto Saha
Databases & web servers
PPIMpred
Web serverHigh-throughput screening of small molecules targeting protein–protein interactions.
R. Soc. Open Sci., 2017LMDIPred
PredictorPredicts linear peptide sequences binding SH3, WW, and PDZ domains.
PLoS ONE, 2018LMPID
DatabaseCurated database of linear motifs mediating protein–protein interactions.
Database, 2015MYCbase
DatabaseFunctional sites and biochemical properties of Myc in normal and cancer cells.
BMC Bioinformatics, 2017PSCRIdb
DatabaseRegulatory interactions and networks of pluripotent stem cell lines.
J. Biosci., 2020DAAB / DAAB-V2
DatabaseCurated database of allergy and asthma biomarkers.
Clin. Exp. Allergy 2015 · Allergy 2021RHO Database
DatabaseBacterial ring-hydroxylating oxygenases for bioremediation & biocatalysis.
Environ. Microbiol. Rep., 2014
Repositories
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cftr-modulator-designEnd-to-end QSAR, virtual screening, molecular docking, and lead-prioritization pipeline for CFTR modulators.
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ppimic50predBioactivity prediction tool.
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profileThis academic profile site.
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llm-mapllm-map.
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AICodeGeneratorAI code generator.
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ChemQuestChemical intelligence, properties, literature, and AI summary.
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github.nsqip.ioACS NSQIP data analysis notebooks.
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sarcoid_RNAseq_analysisSarcoidosis RNA-seq analysis.
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abdpredABDpred — prediction server for antimicrobial compounds.
Peer-reviewed papers
- Indian J. Med. Res. 2024
Jana, T., Sarkar, D., Ganguli, D., Mukherjee, S. K., Mandal, R. S. & Das, S. ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques. 159, 78–90. - Allergy 2021
Majumdar, S., Bhattacharjee, S., Jana, T. & Saha, S. DAAB-V2: Updated database of allergy and asthma biomarkers. 45, 1. - J. Biosci. 2020
Banerjee, K., Jana, T., Ghosh, Z. & Saha, S. PSCRIdb: A database of regulatory interactions and networks of pluripotent stem cell lines. 45, 1. - PLoS ONE 2018
Sarkar, D., Jana, T. & Saha, S. LMDIPred: A web server for predicting linear peptide sequences that bind to SH3, WW, and PDZ domains. 13, e0200430. - BMC Bioinformatics 2017
Chakravorty, D., Jana, T., Mandal, S. D., Seth, A., Bhattacharya, A. & Saha, S. MYCbase: A database of functional sites and biochemical properties of Myc in both normal and cancer cells. 18, 1. - R. Soc. Open Sci. 2017
Jana, T., Ghosh, A., Mandal, S. D., Banerjee, R. & Saha, S. PPIMpred: A web server for high-throughput screening of small molecules targeting protein–protein interactions. 4, 160501. - Clin. Exp. Allergy 2015
Sircar, G., Saha, B., Jana, T., Dasgupta, A., Bhattacharya, S. G. & Saha, S. DAAB: A manually curated database of allergy and asthma biomarkers. 45, 1259–1261. - PLoS ONE 2015
Barman, R. K., Jana, T., Das, S. & Saha, S. Prediction of intra-species protein–protein interactions in enteropathogens facilitating systems biology study. 10, e0143774. - Database 2015
Sarkar, D., Jana, T. & Saha, S. LMPID: A manually curated database of linear motifs mediating protein–protein interactions. 2015, bav101. - Environ. Microbiol. Rep. 2014
Chakraborty, J., Jana, T., Saha, S. & Dutta, T. K. Ring-hydroxylating oxygenase database: A database of bacterial aromatic ring-hydroxylating oxygenases in bioremediation and biocatalysis. 6, 519–523.
- In preparation — Jana, T., Raghav, P. K., Gente, G. & Rajalingam, R. Predicting cross-reactive epitope groups in HLA class I and II using machine learning.
- In preparation — Jana, T., Koth, L. & Ansel, M. Systematic computational mapping of immune cells from bronchoalveolar lavage and peripheral blood in sarcoidosis at single-cell resolution.
- In preparation — Jana, T. & Gabriel, R. A. Machine learning approaches to predicting same-day discharge after total hip arthroplasty using ACS NSQIP data.
- In preparation — Jana, T., Karmakar, J., Banerjee, R. & Saha, S. Supervised learning-driven prediction of small-molecule modulator activity against protein–protein interactions.
Full, continuously updated list with citation counts on Google Scholar.