Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr;115(4):745-757.
doi: 10.1002/cpt.3105. Epub 2024 Jan 31.

Methodology for Good Machine Learning with Multi-Omics Data

Thibaud Coroller  1 , Berkman Sahiner  2 , Anup Amatya  3 , Alexej Gossmann  2 , Konstantinos Karagiannis  4 , Conor Moloney  5 , Ravi K Samala  2 , Luis Santana-Quintero  4 , Nadia Solovieff  1 , Craig Wang  5 , Laleh Amiri-Kordestani  3 , Qian Cao  2 , Kenny H Cha  2 , Rosane Charlab  3 , Frank H Cross Jr  3 , Tingting Hu  2 , Ruihao Huang  3 , Jeffrey Kraft  3 , Peter Krusche  5 , Yutong Li  1 , Zheng Li  1 , Ilya Mazo  4 , Rahul Paul  4 , Susan Schnakenberg  1 , Paolo Serra  1 , Sean Smith  4 , Chi Song  3 , Fei Su  1 , Mohit Tiwari  3 , Colin Vechery  1 , Xin Xiong  2 , Juan Pablo Zarate  1 , Hao Zhu  3 , Arunava Chakravartty  1 , Qi Liu  3 , David Ohlssen  1 , Nicholas Petrick  2 , Julie A Schneider  6 , Mark Walderhaug  4 , Emmanuel Zuber  5
Affiliations

Methodology for Good Machine Learning with Multi-Omics Data

Thibaud Coroller et al. Clin Pharmacol Ther. 2024 Apr.

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.

PubMed Disclaimer

References

    1. Coroller, T.P. et al. Multi-omics investigation on the prognostic and predictive factors in metastatic breast cancer using data from Phase III ribociclib clinical trials: A statistical and machine learning analysis plan. medRxiv 2023-08 (2023).
    1. Diao, J.A. et al. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat. Commun. 12, 1613 (2021).
    1. Libbrecht, M.W. & Noble, W.S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321-332 (2015).
    1. Dvijotham, K. et al. Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians. Nat. Med. 1-7, 1814-1820 (2023).
    1. Lambin, P. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749-762 (2017).

LinkOut - more resources