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Semenov Lab

Clinical Informatics and AI for Healthcare

The Semenov lab at the Massachusetts General Hospital leverages advanced machine learning and multi-modal data fusion approaches to enable precision medicine at the interface or dermatology, oncology, and computational biology. Our lab aims to develop personalized approaches to cancer prognostication, treatment response, and treatment toxicity development leveraging a wide range of data sources, including digital pathology, multiplex imaging, genomics, spatial transcriptomics, population-level registries, and high-throughput electronic health record data. 


We are affiliated with Harvard Data Science Initiative, Department of Biomedical Informatics, Broad Institute, and Dana Farber/Harvard Cancer Center. 


Contacts: 

Email: ysemenov<at>mgh.harvard.edu

Location: 399 Revolution Drive, Suite 1120, Somerville, MA 02145


Administrative Assistant: 

Laurie Frank: lfrank2<at>mgh.harvard.edu

DR. YEVGENIY (“EUGENE”) R. SEMENOV

Yevgeniy (“Eugene”) R. Semenov, MD, MA is a board-certified dermatologist and co-Director of the Oncodermatology Program at Mass General Hospital, and Assistant Professor in Dermatology at Harvard Medical School. He has extensive expertise and training in clinical informatics, health economics, and decision analytics. Dr. Semenov’s research focuses on developing precision medicine tools in dermatology and cutaneous oncology, with a particular interest in prognosticating immunotherapy response and toxicity development, particularly in early-stage melanoma. 


LinkedIn | X | Harvard Catalyst | Github | ORCID | Google Scholar | NCBI

Research Areas

Immunotherapy outcomes

Melanoma prognostication

Integrating clinical, genetic, and histopathologic imaging data  for predicting development of immunotherapy toxicities

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Melanoma prognostication

Melanoma prognostication

Machine learning approaches to improving the prognostication of melanoma recurrence and melanoma-specific survival

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Multi-modal data fusion

Multi-modal data fusion

Multimodal approach to understand melanoma and improve mechanistic explainability of imaging analysis models. 

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Our Team

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