Adversarial Controls for Scientific Machine Learning.

Abstract:

:New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration. Machine learning algorithms readily exploit confounding variables and experimental artifacts instead of relevant patterns, leading to overoptimistic performance and poor model generalization. In parallel to the strong control experiments that remain a cornerstone of experimental research, we advance the concept of adversarial controls for scientific machine learning: the design of exacting and purposeful experiments to ensure that predictive performance arises from meaningful models.

journal_name

ACS Chem Biol

journal_title

ACS chemical biology

authors

Chuang KV,Keiser MJ

doi

10.1021/acschembio.8b00881

subject

Has Abstract

pub_date

2018-10-19 00:00:00

pages

2819-2821

issue

10

eissn

1554-8929

issn

1554-8937

journal_volume

13

pub_type

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