Funded Research
We invest in teams of scientists who are harnessing the power of computational biology research to accelerate discoveries that will improve health outcomes for millions of people.
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Computational Biology
Biswas Family Foundation empowers computational biology research, leveraging advanced data analysis, modeling, and algorithms to transform the way we study, diagnose, and treat disease. By harnessing the power of computation, we aim to accelerate discoveries that will improve health outcomes for millions worldwide.
Transformative Computational Biology Grants
In March 2024, five research teams were selected to receive funding for Biswas Family Foundation’s Transformative Computational Biology Grant Program. As part of a $15 million funding round, multidisciplinary research teams were awarded up to $1 million over two years for research using computational tools across a range of focus areas, including artificial intelligence for genomic medicine, diagnosis of cardiovascular disease, precision oncology therapies, enhancement of clinical datasets, and drug repurposing systems.
Artificial intelligence for Genomic Medicine: Circuitry, Treatment, Personalization
Principal Investigator: Manolis Kellis, PhD, Professor of Computer Science, Massachusetts Institute of Technology
Co-Investigators: Brad Pentelute, PhD, Professor of Chemistry, Massachusetts Institute of Technology, Marinka Zitnik, PhD, Assistant Professor of Biomedical Informatics, Harvard Medical School
This project aims to support the integration of single-cell and spatial sequencing with machine learning approaches to predict target genes for precision therapeutics in cancer, neuroscience, and metabolic disorders. The high-throughput interdisciplinary discovery loop will help advance biomarker selection, de novo drug synthesis, and drug repurposing, which are essential for more effective therapeutics.
A Chatbot Assistant for Genetic Diagnosis and Interpretation of Common and Rare Cardiovascular Diseases
Principal Investigator: Anshul Kundaje, PhD, Associate Professor of Genetics and Computer Science, Stanford University
Co-Investigator: Jesse Engreitz, PhD, Assistant Professor of Genetics, Stanford University
This project aims to accelerate the diagnosis of patients with cardiovascular diseases by developing an artificial intelligence chatbot interfaced with genomic knowledge bases. In the clinical diagnostic workflow, the impact of genomic mutations is unknown in many cardiovascular diseases, so chatbots with access to genomic knowledge bases populated by machine learning models trained on multiple types of data will improve the diagnostic process.
Biswas Center for Transformative Computational Cancer Biology
Principal Investigator: Katherine Pollard, PhD, Director of Data Science & Biotechnology, Gladstone Institutes
Co-Investigators: Alex Marson, MD, PhD, Barbara Engelhardt, PhD, Catherine Tcheandjieu Gueliatcha, DVM, PhD, Christina Theodoris, MD, PhD, Karin Pelka, PhD, Ryan Corces, PhD, Seth Shipman, PhD, and Vijay Ramani, PhD
This project aims to support the development of personalized diagnosis and treatment for colorectal and skin cancers. Machine learning models will be trained to predict how a patient’s genetic mutation can alter tumor cell biology to better understand the diverse pathways that drive cancer, and to evaluate the effectiveness of candidate immunotherapies for more personalized treatments.
The MAIDA Initiative: Democratizing Global Medical Imaging Data Sharing for Safer and Fairer artificial intelligence
Principal Investigator: Pranav Rajpurkar, PhD, Assistant Professor of Biomedical Informatics, Harvard Medical School
This project aims to collect medical imaging data from around the globe to facilitate the use of artificial intelligence to analyze images and improve diagnosis and treatment. The focus is on collecting chest X-rays and chest CT images for a variety of clinical settings. Developing an open data repository of medical images that is representative of diverse patient populations will help the deployment of artificial intelligence tools that are reliable, equitable, and inclusive.
CURE-Bench: Universal Benchmark for All-Disease Drug Repurposing
Principal Investigator: Marinka Zitnik, PhD, Assistant Professor of Biomedical Informatics, Harvard Medical School
This project aims to build CURE-Bench, a comprehensive all-disease benchmark for evaluating computational drug repurposing systems. An international competition will help establish common tasks and datasets to promote the development, evaluation, and widespread use of artificial intelligence models to identify clinically-relevant drug hits. The goal is to develop foundation artificial intelligence models and an accompanying evaluation framework to benchmark models across diseases.
Huang Y, Su X, Ullanat V, Liang I, Clegg L, Olabode D, Ho N, John B, Gibbs M, Zitnik M. Multimodal AI predicts clinical outcomes of drug combinations from preclinical data. arXiv. 2025. doi: 10.48550/arXiv.2503.02781
Velez-Arce A, Lin X, Li MM, Huang K, Gao W, Fu T, Pentelute BL, Kellis M, Zitnik M. Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics. NeurIPS AIDrugX. 2024.
Fang A, Desgagné M, Zhang Z, Zhou A, Loscalzo J, Pentelute, BL, Zitnik M. Learning Universal Representations of Intermolecular Interactions with ATOMICA. bioRxiv. 2025. doi: 10.1101/2025.04.02.646906
Kong Z, Qiu M, Boesen J, Lin X, Yun S, Chen T, Kellis M, Zitnik M. SPATIA: Multimodal Model for Prediction and Generation of Spatial Cell Phenotypes. arXiv. 2025.
Shen W, Nguyen TH, Li MM, Huang Y, Moon I, Nair N, Marbach D, Zitnik M. Generalizable AI predicts immunotherapy outcomes across cancers and treatments. medRxiv. 2025.
Laursen R, Chen H, Demaray J, Pelka K, Engelhardt BE. Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data. bioRxiv. 2025.
Hawthorne O, Ma J, Zhang X, Chen L, Rajpurkar P. ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports. Hugging Face. 2025.
Pal A, Lee JO, Zhang X, Sankarasubbu M, Roh S, Kim WJ, Lee M, Rajpurkar P. ReXVQA dataset. Hugging Face. 2025.
Pal A, Lee JO, Zhang X, Sankarasubbu M, Roh S, Kim WJ, Lee M, Rajpurkar P. ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding. arXiv. 2025.
Zhang X, Acosta JN, Miller J, Huang O, Rajpurkar P. ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports. arXiv. 2025.
Li MM, Li K, Ektefaie Y, Jin Y, Huang Y, Messica S, Cai T, Zitnik M. Controllable Sequence Editing for Biological and Clinical Trajectories. arXiv. 2025. doi: 10.48550/arXiv.2502.03569
Li MM, Reis BY, Rodman A, Cai T, Dagan N, Balicer RD, Loscalzo J, Kohane IS, Zitnik M. One Patient, Many Contexts: Scaling Medical AI Through Contextual Intelligence. arXiv. 2025.
Johnson R, Gottlieb U, Shaham G, Eisen L, Waxman J, Devons-Sberro S, Ginder CR, Hong P, Sayeed R, Su X, Reis BY, Balicer RD, Dagan N, Zitnik M. ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine. medRxiv. 2024. doi: 10.1101/2024.12.03.24318322
Su X, Messica S, Huang Y, Johnson R, Fesser L, Gao S, Sahneh F, Zitnik M. Multimodal medical code tokenizer. arXiv. 2025. doi: 10.48550/arXiv.2502.04397
Gao S, Zhu R, Kong Z, Noori A, Su X, Ginder C, Tsiligkaridis T, Zitnik M. TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools. GitHub. 2025.
Gao S, Zhu R, Kong Z, Noori A, Su X, Ginder C, Tsiligkaridis T, Zitnik M. TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools. arXiv. 2025. doi: 10.48550/arXiv.2503.10970
Su X, Wang Y, Gao S, Liu X, Giunchiglia V, Clevert DA, Zitnik M. KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA. International Conference on Learning Representations, ICLR. 2025.
Gao S, Fang A, Huang Y, Giunchiglia V, Noori A, Schwarz JR, Ektefaie Y, Kondic J, Zitnik M. Empowering biomedical discovery with AI agents. Cell. 2024. doi: 10.1016/j.cell.2024.09.022
Verma A, Yu C, Bachl S, Lopez I, Schwartz M, Moen E, Kale N, Ching C, Miller G, Dougherty T, Pao E, Graf W, Ward C, Jena S, Marson A, Carnevale J, Van Valen D, Engelhardt BE. Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions. bioRxiv. 2024. doi: 10.1101/2024.11.19.624390
Chen H, Venkatesh MS, Ortega JG, Mahesh SV, Nandi TN, Madduri RK, Pelka K, Theodoris CV. Quantized multi-task learning for context-specific representations of gene network dynamics. bioRxiv. 2024. doi: 10.1101/2024.08.16.608180
Queen O, Huang Y, Calef R, Giunchiglia V, Chen T, Dasoulas G, Tai L, Ektefaie Y, Noori A, Brown J, Cobley T, Hrovatin K, Hartvigsen T, Theis FJ, Pentelute B, Khurana V, Kellis M, Zitnik M. ProCyon: A multimodal foundation model for protein phenotypes. bioRxiv. 2024. doi:10.1101/2024.12.10.627665
Johnson R, Gottlieb U, Shaham G, Eisen L, Waxman J, Devons-Sberro S, Ginder CR, Hong P, Sayeed R, Reis BY, Balicer RD, Dagan N, Zitnik M. Unified Clinical Vocabulary Embeddings for Advancing Precision. medRxiv. 2024. doi: 10.1101/2024.12.03.24318322v2
Zhang Z, Shen WX, Liu Q, Zitnik M. Efficient generation of protein pockets with PocketGen. Nature Machine Intelligence. 2024. doi: 10.1038/s42256-024-00920-9
Lin MM, Huang Y, Sumathipala M, Liang MQ, Valdeolivas A, Ananthakrishnan AN, Liao K, Marbach D, Zitnik M. Contextual AI models for single-cell protein biology. Nature Methods. 2024. doi:10.1038/s41592-024-02341-3
Li MM, Huang Y, Sumathipala M, Liang MQ, Valdeolivas A, Ananthakrishnan A, Liao K, Marbach D, Zitnik M. Contextual AI models for single-cell protein biology. GitHub. 2024
Velez-Arce A, Lin X, Li MM, Huang K, Gao W, Fu T, Pentelute BL, Kellis M, Zitnik M. Therapeutics Data Commons. GitHub. 2024.
Huang K, Chandak P, Wang Q, Havaldar S, Vaid A, Leskovec J, Nadkarni G, Glicksberg BS, Gehlenborg N, Zitnik M. A foundation model for clinician-centered drug repurposing. Nature Medicine. 2024. doi: 10.1038/s41591-024-03233-x
Banerjee O, Saenz A, Wu K, Clements W, Zia A, Buensalido D, Kavnoudias H, Abi-Ghanem AS, El Ghawi N, Luna C, Castillo P, Al-Surimi K, Daghistani RA, Chen YM, Chao H, Heiliger L, Kim M, Haubold J, Jonske F, Rajpurkar P. ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics. arXiv. 2024. doi: 10.48550/arXiv.2408.16208
Drusinsky S, Whalen S, Pollard KS. Deep-learning prediction of gene expression from personal genomes. bioRxiv. 2024. doi: 10.1101/2024.07.27.605449
Organizational Support
We give to groups that advance computational biology, foster scientific curiosity, and promote collaboration.
Arc Institute
United States
As part of the $15 million in funding announced in March 2024, the Foundation provided a gift to the Arc Institute to enhance artificial intelligence and computational biology research.
Arc Institute is a nonprofit research organization whose mission is to accelerate scientific progress and understand the root causes of complex diseases. By doing this, they aim to improve human health by narrowing the gap between discoveries and impact on patients.
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