Projects

4CE: Consortium for Clinical Characterization of COVID-19 by EHR. In collaboration with the i2b2 Foundation, we have convened this international volunteer consortium across 9 countries and more than 300 hospitals to study the clinical course of COVID from early 2020 to the present. Now also investigating “long COVID.”

Funded by the NIH Common Fund, the 4DNucleome (4DN) Data Coordination and Integration Center (DCIC) Data Portal supports study of the three-dimensional organization of the nucleus in space and time (“the 4th dimension”) by collecting, storing, curating, displaying and analyzing data generated by the 4DN Network.

Our new AIM PhD track will train exceptional computational students, harnessing large-scale biomedical data and cutting-edge AI methods, to create new technologies and clinically impactful research that transform medicine around the world, increasing both the quality and equity of health outcomes.

The National Institutes of Health AIM-AHEAD program was established to ensure better health for all through artificial intelligence and machine learning (AI/ML) research. The program’s Infrastructure Core facilitates this research by assessing data, computing, and software infrastructure.

Made possible by a generous philanthropic gift by Ivan and Francesca Berkowitz and Family, this collaboration between DBMI and Clalit Research Institute leverages Israel's largest health insurance and medical provider as a living laboratory to yield insights and fuel therapies that ripple beyond borders and benefit people across the globe.

The Core for Computational Biomedicine (CCB) is a multi-disciplinary team of computational and quantitative scientists that collaborates with HMS Quad-based researchers to develop tools, platforms, and other data science and computational solutions that broadly enable biomedical discovery and innovation.

CGAP transforms sequencing data into actionable genetic insights via an intuitive, open-source analysis tool designed to support complex research & clinical genomics workflows.

Funded by the National Institute on Aging, The Confluence Projects study a complex assemblage of geospatial factors, such as extreme heat and cold, that will increasingly impact human health and demographic outcomes related to aging, such as dementia.

CHARM is an AI tool that enables in-surgery genomic profiling of gliomas, the most aggressive and most common brain tumors, providing real-time guidance to surgeons on the optimal surgical approach for removal of cancerous tissue.

genTB is an analysis tool for Mycobacterium tuberculosis genomic data that offers three main features: a means for sharing, citing and crediting TB data and metadata, the prediction of resistance on genotype using a machine learning algorithm, and geographic resistance and mutation data mapping.

The Human Biomolecular Atlas Program (HuBMAP) Data Portal is the central resource for discovery, visualization, and download of single-cell tissue data generated by the HuBMAP Consortium. A standardized data curation and processing workflow ensure that only high-quality is released.

The IGVF (Impact of Genomic Variation on Function) Consortium aims to understand how genomic variation affects genome function, which in turn impacts phenotype. The Predictive Modeling project led by DBMI faculty is developing tools that will enable a mechanistic understanding of a broad spectrum of human diseases.

MAIDA is a comprehensive data set of patient radiology images being created by an international partnership initiative. 80 hospitals from 34 countries have already contributed.

The MOMA platform is an explainable machine learning approach to systematically identify and interpret the relationship between colorectal cancer (CRC) patients' histologic patterns, multi-omics, and clinical profiles.

NEXUS, a Center for Exposome Research Coordination (CERC) funded by the National Institute of Environmental Health Sciences (NIEHS) and other federal agencies, is leading a movement to understand how our environments, or the exposome, shape human health.

For research investigators who need to find, access, share, store, and compute on large scale datasets, BDC serves as a cloud-based ecosystem providing tools, applications, and workflows to enable these capabilities in secure workspaces.

People Heart Study, part of the People-Powered Medicine project, enables individuals to use their personal health data to learn about their risk of heart disease and discuss results with their doctor—leading to more informed, shared decision making.

Since our founding in 2015, DBMI has convened expert scientists from academia and industry along with patient leaders to advance precision medicine for all populations. We recently hosted our tenth annual conference, focused on precision medical education and generative AI.

Healthcare needs a global discussion of how best to use this revolutionary new wave of AI. The framework created from our 2023 symposium by the international consortium we brought together has been published in NEJM AI and Nature Medicine.

Rheumatoid arthritis (RA) is one of many medical conditions being redefined as scientific advancements improve our ability to study underlying genetics and pathways to target for therapy. RANT, part of the People-Powered Medicine (PPM) project, is studying why RA therapies work for some patients and not others.

A platform to search, visualize, and download somatic mosaic variants in normal tissues as part of the NIH-funded SMaHT Network.

SAIL is an international conference launched in 2020 to explore the integration of artificial intelligence (AI) techniques into clinical medicine.

Therapeutics Data Commons is a global initiative to access and evaluate artificial intelligence capability across therapeutic modalities and stages of discovery.

The Undiagnosed Diseases Network (UDN) is a research study backed by the National Institutes of Health that seeks to provide answers for patients and families affected by mysterious conditions.