Kun-Hsing Yu, MD, PhD

Kun-Hsing Yu, MD, PhD

Assistant Professor of Biomedical Informatics, Harvard Medical School
Assistant Professor of Pathology, Brigham and Women's Hospital
Instructor in Epidemiology, Harvard T.H. Chan School of Public Health
Yu Lab

Kun-Hsing "Kun" Yu received his PhD in Biomedical Informatics and PhD Minor in Computer Science from Stanford University, and he obtained his MD from National Taiwan University, Taiwan. His research focuses on the integration of quantitative histopathology image patterns with multi-omics (genomics, epigenomics, transcriptomics, and proteomics) profiles to advance cancer research and clinical practice. His team has developed fully-automated algorithms to analyze whole-slide histopathology images at scale, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and identified novel cellular morphologies for patient prognosis. His research interests include quantitative pathology, machine learning, and translational bioinformatics.

DBMI Research Areas
DBMI Courses
Current Postdoctoral Fellowship Opportunities
Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.
Authors: McAvoy M, Prieto PC, Kaczmarzyk JR, Fernández IS, McNulty J, Smith T, Yu KH, Gormley WB, Arnaout O.
Sci Rep
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Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma.
Authors: Marostica E, Barber R, Denize T, Kohane IS, Signoretti S, Golden JA, Yu KH.
Clin Cancer Res
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Epidemiology and risk factors for the development of cutaneous toxicities in patients treated with immune-checkpoint inhibitors: A United States population-level analysis.
Authors: Wongvibulsin S, Pahalyants V, Kalinich M, Murphy W, Yu KH, Wang F, Chen ST, Reynolds K, Kwatra SG, Semenov YR.
J Am Acad Dermatol
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Prediction of severe immune-related adverse events requiring hospital admission in patients on immune checkpoint inhibitors: study of a population level insurance claims database from the USA.
Authors: Kalinich M, Murphy W, Wongvibulsin S, Pahalyants V, Yu KH, Lu C, Wang F, Zubiri L, Naranbhai V, Gusev A, Kwatra SG, Reynolds KL, Semenov YR.
J Immunother Cancer
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Temporal bias in case-control design: preventing reliable predictions of the future.
Authors: Yuan W, Beaulieu-Jones BK, Yu KH, Lipnick SL, Palmer N, Loscalzo J, Cai T, Kohane IS.
Nat Commun
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Prolonged Auditory Brainstem Response in Universal Hearing Screening of Newborns with Autism Spectrum Disorder.
Authors: Miron O, Delgado RE, Delgado CF, Simpson EA, Yu KH, Gutierrez A, Zeng G, Gerstenberger JN, Kohane IS.
Autism Res
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Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.
Authors: Sun Y, Zheng W, Zhang L, Zhao H, Li X, Zhang C, Ma W, Tian D, Yu KH, Xiao S, Jin L, Hua J.
Front Endocrinol (Lausanne)
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Deep decision support for lymph node metastatic risk evaluation.
Authors: Marostica E, Yu KH.
EBioMedicine
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Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks.
Authors: Yu KH, Hu V, Wang F, Matulonis UA, Mutter GL, Golden JA, Kohane IS.
BMC Med
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Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.
Authors: Yu KH, Lee TM, Yen MH, Kou SC, Rosen B, Chiang JH, Kohane IS.
J Med Internet Res
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