A review of active learning approaches to experimental design for uncovering biological networks.

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory expe...

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Main Authors:	Yuriy Sverchkov, Mark Craven
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2017-06-01
Series:	PLoS Computational Biology
Online Access:	http://europepmc.org/articles/PMC5453429?pdf=render

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spelling	doaj-a9e108e4220f49bb908b36cf82b878152020-11-25T01:18:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-06-01136e100546610.1371/journal.pcbi.1005466A review of active learning approaches to experimental design for uncovering biological networks.Yuriy SverchkovMark CravenVarious types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area.http://europepmc.org/articles/PMC5453429?pdf=render
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yuriy Sverchkov Mark Craven
spellingShingle	Yuriy Sverchkov Mark Craven A review of active learning approaches to experimental design for uncovering biological networks. PLoS Computational Biology
author_facet	Yuriy Sverchkov Mark Craven
author_sort	Yuriy Sverchkov
title	A review of active learning approaches to experimental design for uncovering biological networks.
title_short	A review of active learning approaches to experimental design for uncovering biological networks.
title_full	A review of active learning approaches to experimental design for uncovering biological networks.
title_fullStr	A review of active learning approaches to experimental design for uncovering biological networks.
title_full_unstemmed	A review of active learning approaches to experimental design for uncovering biological networks.
title_sort	review of active learning approaches to experimental design for uncovering biological networks.
publisher	Public Library of Science (PLoS)
series	PLoS Computational Biology
issn	1553-734X 1553-7358
publishDate	2017-06-01
description	Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area.
url	http://europepmc.org/articles/PMC5453429?pdf=render
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A review of active learning approaches to experimental design for uncovering biological networks.

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