{"id":852672,"date":"2022-06-17T11:56:25","date_gmt":"2022-06-17T18:56:25","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=852672"},"modified":"2024-01-08T16:22:43","modified_gmt":"2024-01-09T00:22:43","slug":"biomedical-computing","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/biomedical-computing\/","title":{"rendered":"Biomedical Computing"},"content":{"rendered":"
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Biomedical Computing<\/h1>\n\n\n\n

Making the promise of genomic medicine a reality<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

The Biomedical Computing mission<\/h2>\n\n\n\n

The volume of biomedical data is growing exponentially, creating a need for new tools and workflows to understand the underlying biological processes these data represent. We aim to accelerate the pace of precision health research and clinical translation through the digitization of data, computational analysis, and cloud capabilities.<\/p>\n\n\n\n

The Biomedical Computing team strives to accomplish this mission by leveraging the strengths of an interdisciplinary team of experts specializing in biology, computer science, computational biology, machine learning, software and hardware development, human subjects research and ethics, electrical engineering, and bioengineering.<\/p>\n\n\n\n

The Broad Institute – Microsoft Research collaboration<\/h3>\n\n\n\n

Joining forces with industry leading, cutting-edge health and life sciences researchers at the Broad Institute of MIT and Harvard, this collaboration aims to help life science researchers and clinicians with the next generation of robust analytical methods and tools that are highly scalable, easily automated, and leverage cutting-edge machine learning techniques.<\/p>\n\n\n\n

The new collaboration is designed to meet growing challenges and opportunities in biology and medicine. Life scientists are generating more biomedical data than ever and yet are struggling to scale existing methods and tools to analyze such large amounts of data. Most existing biomedical research tools were designed for small-scale work.<\/p>\n\n\n\n

However, rapid developments in technology over the last decade are now enabling researchers to deeply investigate the molecular mechanisms of life from fresh angles. New advances in machine learning and data science offer exciting opportunities for researchers to gain more insight from a greater variety of large datasets. This will improve our understanding of the origins of disease and lead to the development of better diagnostics and treatments.<\/p>\n\n\n\n

Read the news story here ><\/span> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n

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Focus areas<\/h2>\n\n\n\n
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Cloud-native tooling<\/h4>\n\n\n\n

Increasing the accessibility and utility of biomedical data by optimizing pipelines for efficient and scalable analysis, enabling cost-effective cloud storage and compute, and assembling resources for convenience and reduced latency.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Sequencing technology<\/h4>\n\n\n\n

Advancing the state of sequencing by exploring new sequencing modalities, engineering technologies to enhance the value of sequencing, and enabling population health research with secure and streamlined technology and workflows.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Rare disease diagnostics<\/h4>\n\n\n\n

Helping to shorten the rare disease diagnostic odyssey by developing tools and pipelines to improve the identification and interpretation of potentially disease-causing genetic variants in the genomes of families affected by rare genetic disorders.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"

Accelerating the digital transformation of precision health by developing new technologies to understand the biology behind biomedical data.<\/p>\n","protected":false},"featured_media":862341,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13553],"msr-locale":[268875],"msr-impact-theme":[261673],"msr-pillar":[],"class_list":["post-852672","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[853752],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Sumit Basu","user_id":33754,"people_section":"Section name 0","alias":"sumitb"},{"type":"user_nicename","display_name":"Gabe Cohn","user_id":31836,"people_section":"Section name 0","alias":"gabe"},{"type":"user_nicename","display_name":"Ashley Conard","user_id":42849,"people_section":"Section name 0","alias":"ashleyconard"},{"type":"user_nicename","display_name":"Jonathan Lester","user_id":32333,"people_section":"Section name 0","alias":"jlester"},{"type":"user_nicename","display_name":"Ron Paulsen","user_id":33450,"people_section":"Section name 0","alias":"ropau"},{"type":"user_nicename","display_name":"Scott Saponas","user_id":33715,"people_section":"Section name 0","alias":"ssaponas"},{"type":"user_nicename","display_name":"Greg Smith","user_id":31910,"people_section":"Section name 0","alias":"gregsmi"},{"type":"user_nicename","display_name":"Hope Twede","user_id":40747,"people_section":"Section name 0","alias":"hopetw"},{"type":"user_nicename","display_name":"Jeremiah (Miah) Wander","user_id":32896,"people_section":"Section name 0","alias":"miah"}],"msr_research_lab":[849856],"msr_impact_theme":["Health"],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/852672","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":20,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/852672\/revisions"}],"predecessor-version":[{"id":997872,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/852672\/revisions\/997872"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/862341"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=852672"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=852672"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=852672"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=852672"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=852672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}