Methodology for Good Machine Learning with Multi‐Omics Data

传播 多学科方法 计算机科学 数据科学 分析 平面图(考古学) 模式 知识管理 电信 社会科学 考古 社会学 历史
作者
Thibaud Coroller,Berkman Sahiner,Anup Amatya,Alexej Gossmann,Konstantinos Karagiannis,Conor Moloney,Ravi K. Samala,Luis V. Santana‐Quintero,Nadia Solovieff,Craig Wang,Laleh Amiri‐Kordestani,Qian Cao,H. Kenny,Rosane Charlab,Frank H. Cross,Tingting Hu,Ruihao Huang,Jeffrey Kraft,Peter Krusche,Yutong Li,Zheng Li,Ilya Mazo,Rahul Paul,Susan Schnakenberg,P. Serra,Sean Smith,Chi Song,Fei Su,Mohit Tiwari,Colin Vechery,Xin Xiong,Juan Pablo Zarate,Hao Zhu,Arunava Chakravartty,Qi Liu,David Ohlssen,Nicholas Petrick,Julie A. Schneider,Mark Walderhaug,Emmanuel Zuber
出处
期刊:Clinical Pharmacology & Therapeutics [Wiley]
卷期号:115 (4): 745-757 被引量:4
标识
DOI:10.1002/cpt.3105
摘要

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.
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