Active machine learning-driven experimentation to determine compound effects on protein patterns

Active machine learning-driven experimentation to determine compound effects on protein patterns

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experiment...

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Bibliographic Details
Main Authors: Armaghan W Naik, Joshua D Kangas, Devin P Sullivan, Robert F Murphy
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2016-02-01
Series:eLife
Subjects:
active learning
protein subcellular location
laboratory automation
high content screening
automation of research
machine learning
Online Access:https://elifesciences.org/articles/10047

Internet

https://elifesciences.org/articles/10047

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