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      1. Disease areas Ó³»­´«Ã½ brings people together to advance the understanding and treatment of disease.
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      2. Research areas Through programs spanning genetics, biology, and therapeutic development, Ó³»­´«Ã½ researchers are making discoveries that drive biomedical science forward.
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        1. Patient-partnered research Patients partner with our scientists to accelerate the pace of discovery and find better treatments.
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        3. Publications A catalog of scientific papers published by our members and staff scientists.
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        5. Collaborations and consortia We join with institutions and scientists the world over to address foundational challenges in science and health.
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      1. Carlos Slim Center for Health Research The Slim Center aims to bring the benefits of genomics-driven medicine to Latin America, gleaning new insights into diseases with relevance to the region.
      2. Gerstner Center for Cancer Diagnostics The Gerstner Center is developing next-generation diagnostic technology for cancer detection and tracking disease progression.
      3. Klarman Cell Observatory The Klarman Cell Observatory is systematically defining mammalian cellular circuits, how they work together to create tissues and organs, and are perturbed to cause disease.
      4. Merkin Institute for Transformative Technologies in Healthcare The Merkin Institute is supporting early-stage ideas aimed at advancing powerful technological approaches for improving how we understand and treat disease.
      5. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease This center is developing new paradigms and technologies to scale the discovery of biological mechanisms of common, complex diseases, by facilitating close collaborations between the Ó³»­´«Ã½ and the Danish research community.
      6. Eric and Wendy Schmidt Center The EWSC is catalyzing a new field of interdisciplinary research at the intersection of data science and life science, aimed at improving human health.
      7. Stanley Center for Psychiatric Research The Stanley Center aims to reduce the burden of serious mental illness by contributing new insights into pathogenesis, identifying biomarkers, and paving the way toward new treatments.
  • Education and outreach
      1. Art and science connection Explore the connection between art and science and how we bring together artists and Ó³»­´«Ã½ scientists through our artist-in-residence program, gallery exhibitions, and ongoing public conversations.
      2. Ó³»­´«Ã½ Discovery Center Visit our free public educational space that showcases how researchers at the Ó³»­´«Ã½ and their colleagues around the world seek to understand and treat human disease.
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      4. Public programs Discover remarkable stories of scientific progress, and explore the intersections of science, medicine, and society.
      5. Student opportunities Learn about Ó³»­´«Ã½'s mentored research offerings for high school students, college students, and recent college graduates.
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New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals.
Kany S, Rämö JT, Playford D, et al. New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals. Journal of the American College of Cardiology. 2025;85(13):1387-1399. doi:10.1016/j.jacc.2025.01.035
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Refining the cis-regulatory grammar learned by sequence-to-activity models by increasing model resolution.
Chandra NA, Hu Y, Buenrostro JD, Mostafavi S, Sasse A. Refining the cis-regulatory grammar learned by sequence-to-activity models by increasing model resolution. bioRxiv : the preprint server for biology. 2025. doi:10.1101/2025.01.24.634804
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A multi-modal transformer for cell type-agnostic regulatory predictions.
Javed N, Weingarten T, Sehanobish A, et al. A multi-modal transformer for cell type-agnostic regulatory predictions. Cell genomics. 2025:100762. doi:10.1016/j.xgen.2025.100762
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A multi-modal transformer for cell type-agnostic regulatory predictions.
Javed N, Weingarten T, Sehanobish A, et al. A multi-modal transformer for cell type-agnostic regulatory predictions. Cell genomics. 2025:100762. doi:10.1016/j.xgen.2025.100762
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microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.
Zacharopoulou E, Paraskevopoulou MD, Tastsoglou S, et al. microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites. Briefings in bioinformatics. 2024;26(1). doi:10.1093/bib/bbae678
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microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.
Zacharopoulou E, Paraskevopoulou MD, Tastsoglou S, et al. microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites. Briefings in bioinformatics. 2024;26(1). doi:10.1093/bib/bbae678
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Non-Generative Artificial Intelligence (AI) in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning.
Pantanowitz L, Pearce T, Abukhiran I, et al. Non-Generative Artificial Intelligence (AI) in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2024:100680. doi:10.1016/j.modpat.2024.100680
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Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke.
Khurshid S, Friedman SF, Kany S, et al. Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke. Circulation. Arrhythmia and electrophysiology. 2024:e012959. doi:10.1161/CIRCEP.124.012959
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Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke.
Khurshid S, Friedman SF, Kany S, et al. Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke. Circulation. Arrhythmia and electrophysiology. 2024:e012959. doi:10.1161/CIRCEP.124.012959
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Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation.
Raghu A, Raghu A, Wise JF. Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation. JMIR bioinformatics and biotechnology. 2024;5:e56538. doi:10.2196/56538
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In March of 2020, Ó³»­´«Ã½ converted a clinical genetics processing lab into a large-scale COVID-19 testing facility in less than two weeks.

We've screened more than 1,275 cancer cell lines as part of the Cancer Dependency Map (DepMap).

Ó³»­´«Ã½ Genomics Platform sequences a whole human genome every four minutes.

More than 11,000 individuals living with cancer in the United States and Canada have partnered with Count Me In to share their experiences and help accelerate cancer research.

The Drug Repurposing Hub is one of the most comprehensive and up-to-date biologically annotated collections of FDA-approved compounds in the world. Researchers anywhere can explore more than 6,000 drugs in the hub and search for possible new uses for them to jump-start new drug discovery.

In 2021, our sustainability efforts sent more than 80 percent of waste from the Genomics Platform to either a recycling facility or to an incineration plant that generates electricity.

Through Ó³»­´«Ã½'s Scientists in the Classroom program, Ó³»­´«Ã½ researchers visit every 8th grade classroom in Cambridge each year to talk about genetics and evolution.

Every summer, 18 high school students spend six weeks at Ó³»­´«Ã½ working side-by-side with mentors on cutting-edge research.

In November 2022, Ó³»­´«Ã½â€™s Genomics Platform sequenced its 500,000th whole human genome, a mere four years after sequencing its 100,000th.

By the end of 2022, Ó³»­´«Ã½â€™s COVID-19 testing lab had processed more than 37 million tests.

Working with Addgene, Ó³»­´«Ã½ has shared CRISPR genome-editing reagents with researchers at more than 3,200 institutions in 76 countries.

The NeuroGAP-Psychosis project, a collaboration between the Stanley Center for Psychiatric Research and Harvard T.H. Chan School of Public Health to study the genetics of severe mental illness, has recruited more than 42,000 participants in Ethiopia, Kenya, Uganda, and South Africa.

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