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Methodology for Good Machine Learning with Multi‐Omics Data.

Authors :
Coroller, Thibaud
Sahiner, Berkman
Amatya, Anup
Gossmann, Alexej
Karagiannis, Konstantinos
Moloney, Conor
Samala, Ravi K.
Santana‐Quintero, Luis
Solovieff, Nadia
Wang, Craig
Amiri‐Kordestani, Laleh
Cao, Qian
Cha, Kenny H.
Charlab, Rosane
Cross, Frank H.
Hu, Tingting
Huang, Ruihao
Kraft, Jeffrey
Krusche, Peter
Li, Yutong
Source :
Clinical Pharmacology & Therapeutics; Apr2024, Vol. 115 Issue 4, p745-757, 13p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00099236
Volume :
115
Issue :
4
Database :
Complementary Index
Journal :
Clinical Pharmacology & Therapeutics
Publication Type :
Academic Journal
Accession number :
176146223
Full Text :
https://doi.org/10.1002/cpt.3105