illustration of DNA strand and abstract shapes on a computer screen on a green gradient background

Medical, health, and genomics

Digital pathology helps decode tumor microenvironments for precision immunotherapy. GigaPath is a novel vision transformer that can scale to gigapixel whole-slide images by adapting dilated attention for digital pathology. In joint work with Providence and UW, we’re sharing Prov-GigaPath, the first whole-slide pathology foundation model pretrained on large-scale real-world data, for advancing clinical research and discovery.

GigaPath: Whole-Slide Foundation Model for Digital Pathology

Research Forum Ep2 | Panel | Transforming the Natural Sciences with AI

Research Forum 2 | Panel Discussion: Transforming the Natural Sciences with AI

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Scaling early detection of esophageal cancer with AI

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Published Date

Publication

A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions.

Isabel Chien, Angel Enrique, Jorge Palacios, Tim Regan, Dessie Keegan, David Carter, Sebastian Tschiatschek, Aditya Nori, Anja Thieme, Derek Richards, Gavin Doherty, Danielle Belgrave

JAMA Network | June 2020, Vol 3(7)

Project

Publication

Inequity in access to personalized medicine in France: Evidences from analysis of geo variations in the access to molecular profiling among advanced non-small-cell lung cancer patients: Results from the IFCT Biomarkers France Study.

Samuel Kembou Nzale, Bill Weeks, L’Houcine Ouafik, Isabelle Rouquette, Michèle Beau-Faller, Antoinette Lemoine, Pierre-Paul Bringuier, Anne-Gaëlle Le Coroller Soriano, Fabrice Barlesi, Bruno Ventelou

PLOS ONE | June 2020, Vol 15(7)

Publication

Quantifying Molecular Bias in DNA Data Storage

Yuan-Jyue Chen, Christopher N. Takahashi, Lee Organick, Callista Bee, Siena Dumas Ang, Patrick Weiss, Bill Peck, Georg Seelig, Luis Ceze, Karin Strauss

Nature Communications | June 2020, Vol 11(3264)

Project

Group

Digital Mental Health group comprises of an interdisciplinary team of researchers, engineers, data scientists, and designers who are passionate about studying mental health, building technology to support mental health, and improving people’s wellbeing.

Publication

Deep learning for prediction of the air quality response to emission changes

Jia Xing, Shuxin Zheng, Dian Ding, James T Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey J. Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao

Environmental Science & Technology | June 2020, Vol 54(14): pp. 8589-8600

Publication