| The Alzheimer's disease neuroimaging initiative phase 2: increasing the length, breadth, and depth of our understanding LA Beckett, MC Donohue, C Wang, P Aisen, DJ Harvey, N Saito, ... Alzheimer's & Dementia 11 (7), 823-831, 2015 | 86 | 2015 |
| Using machine learning and natural language processing to review and classify the medical literature on cancer susceptibility genes Y Bao, Z Deng, Y Wang, H Kim, VD Armengol, F Acevedo, N Ouardaoui, ... JCO Clinical Cancer Informatics 1, 1-9, 2019 | 40 | 2019 |
| Validation of a semiautomated natural language processing–based procedure for meta-analysis of cancer susceptibility gene penetrance Z Deng, K Yin, Y Bao, VD Armengol, C Wang, A Tiwari, R Barzilay, ... JCO clinical cancer informatics 3, 1-9, 2019 | 29 | 2019 |
| Penetrance of colorectal cancer among mismatch repair gene mutation carriers: a meta-analysis C Wang, Y Wang, KS Hughes, G Parmigiani, D Braun JNCI Cancer Spectrum 4 (5), pkaa027, 2020 | 23 | 2020 |
| Penetrance of male breast cancer susceptibility genes: a systematic review RS Chamseddine, C Wang, K Yin, J Wang, P Singh, J Zhou, ME Robson, ... Breast Cancer Research and Treatment, 1-8, 2022 | 14 | 2022 |
| Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models C Shyr, Y Hu, L Bastarache, A Cheng, R Hamid, P Harris, H Xu Journal of Healthcare Informatics Research, 1-24, 2024 | 8* | 2024 |
| A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption R Wang, A Bing, C Wang, Y Hu, RJ Bosch, V DeGruttola Statistics in medicine 39 (15), 2051-2066, 2020 | 7 | 2020 |
| A validation of models for prediction of pathogenic variants in mismatch repair genes C Shyr, AL Blackford, T Huang, J Ke, N Ouardaoui, L Trippa, S Syngal, ... Genetics in Medicine 24 (10), 2155-2166, 2022 | 2 | 2022 |
| Establishing and evaluating an ASCO learning cohort: A longitudinal project assessing the learning needs and behaviors of oncology professionals R Manochakian, TJ George, M Wood, M Polansky, A Baer, A Grupe, ... Journal of Cancer Education 36, 478-483, 2021 | 2 | 2021 |
| Quantitative assessment of learning behaviors for oncology providers M Wood, TJ George, R Manochakian, M Polansky, A Baer, A Grupe, ... Journal of Cancer Education 36, 25-32, 2021 | 2 | 2021 |
| Quantitative analysis of oncology professional learning preferences TJ George, R Manochakian, M Wood, M Polansky, A Baer, A Grupe, ... JCO Oncology Practice 16 (2), e155-e165, 2020 | 2 | 2020 |
| Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling C Shyr, P Sur, G Parmigiani, P Patil arXiv preprint arXiv:2207.04588, 2022 | 1 | 2022 |
| Evaluation of Lynch syndrome risk models in a multicenter diverse population. J Lu, S Knapp, GG Seymour, C Wang, T Huang, J Liang, K Yang, ... Journal of Clinical Oncology 40 (16_suppl), 10597-10597, 2022 | 1 | 2022 |
| Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program C Shyr, L Sulieman, PA Harris Journal of the American Medical Informatics Association, ocae062, 2024 | | 2024 |
| Multi-study R-learner for Heterogeneous Treatment Effect Estimation C Shyr, B Ren, P Patil, G Parmigiani arXiv preprint arXiv:2306.01086, 2023 | | 2023 |
| Statistical and Machine Learning Methods for Multi-Study Prediction and Causal Inference C Wang Harvard University, 2022 | | 2022 |
| Impact of financial compensation on enrollment and participation in a remote, mobile-app based research study S Meier, A Cheng, M Tischbein, C Shyr, RN Jerome, TL Edwards, ... Journal of Clinical and Translational Science, 1-25, 0 | | |
Giovanni ParmigianiProfessor Department of Data Science, DFCIVerified email at dfci.harvard.edu
