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Your search keyword '"Cha, Kenny H."' showing total 7 results
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7 results on '"Cha, Kenny H."'

1. Methodology for good machine learning with multi‐omics data

Author

Coroller, Thibaud, primary, Sahiner, Berkman, additional, Amatya, Anup, additional, Gossmann, Alexej, additional, Karagiannis, Konstantinos, additional, Moloney, Conor, additional, Samala, Ravi K., additional, Santana‐Quintero, Luis, additional, Solovieff, Nadia, additional, Wang, Craig, additional, Amiri‐Kordestani, Laleh, additional, Cao, Qian, additional, Cha, Kenny H., additional, Charlab, Rosane, additional, Cross Jr, Frank H., additional, Hu, Tingting, additional, Huang, Ruihao, additional, Kraft, Jeffrey, additional, Krusche, Peter, additional, Li, Yutong, additional, Li, Zheng, additional, Mazo, Ilya, additional, Paul, Rahul, additional, Schnakenberg, Susan, additional, Serra, Paolo, additional, Smith, Sean, additional, Song, Chi, additional, Su, Fei, additional, Tiwari, Mohit, additional, Vechery, Colin, additional, Xiong, Xin, additional, Zarate, Juan Pablo, additional, Zhu, Hao, additional, Chakravartty, Arunava, additional, Liu, Qi, additional, Ohlssen, David, additional, Petrick, Nicholas, additional, Schneider, Julie A., additional, Walderhaug, Mark, additional, and Zuber, Emmanuel, additional

Published
2023
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5. Urinary bladder cancer staging in CT urography using machine learning

Author

Garapati, Sankeerth S., primary, Hadjiiski, Lubomir, additional, Cha, Kenny H., additional, Chan, Heang‐Ping, additional, Caoili, Elaine M., additional, Cohan, Richard H., additional, Weizer, Alon, additional, Alva, Ajjai, additional, Paramagul, Chintana, additional, Wei, Jun, additional, and Zhou, Chuan, additional

Published
2017
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7. Methodology for Good Machine Learning with Multi-Omics Data.

Author

Coroller T, Sahiner B, Amatya A, Gossmann A, Karagiannis K, Moloney C, Samala RK, Santana-Quintero L, Solovieff N, Wang C, Amiri-Kordestani L, Cao Q, Cha KH, Charlab R, Cross FH Jr, Hu T, Huang R, Kraft J, Krusche P, Li Y, Li Z, Mazo I, Paul R, Schnakenberg S, Serra P, Smith S, Song C, Su F, Tiwari M, Vechery C, Xiong X, Zarate JP, Zhu H, Chakravartty A, Liu Q, Ohlssen D, Petrick N, Schneider JA, Walderhaug M, and Zuber E

Subjects
Humans, Machine Learning, Genomics, Artificial Intelligence, Multiomics
Abstract

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance., (© 2023 The Authors. Clinical Pharmacology & Therapeutics © 2023 American Society for Clinical Pharmacology and Therapeutics. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Published
2024
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