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Chapter 2: Data-Driven View of Disease Biology

Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and...

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Detalles Bibliográficos
Autores principales: Greene, Casey S., Troyanskaya, Olga G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531282/
https://www.ncbi.nlm.nih.gov/pubmed/23300408
http://dx.doi.org/10.1371/journal.pcbi.1002816
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author Greene, Casey S.
Troyanskaya, Olga G.
author_facet Greene, Casey S.
Troyanskaya, Olga G.
author_sort Greene, Casey S.
collection PubMed
description Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.
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spelling pubmed-35312822013-01-08 Chapter 2: Data-Driven View of Disease Biology Greene, Casey S. Troyanskaya, Olga G. PLoS Comput Biol Education Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease. Public Library of Science 2012-12-27 /pmc/articles/PMC3531282/ /pubmed/23300408 http://dx.doi.org/10.1371/journal.pcbi.1002816 Text en © 2012 Greene, Troyanskaya http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Education
Greene, Casey S.
Troyanskaya, Olga G.
Chapter 2: Data-Driven View of Disease Biology
title Chapter 2: Data-Driven View of Disease Biology
title_full Chapter 2: Data-Driven View of Disease Biology
title_fullStr Chapter 2: Data-Driven View of Disease Biology
title_full_unstemmed Chapter 2: Data-Driven View of Disease Biology
title_short Chapter 2: Data-Driven View of Disease Biology
title_sort chapter 2: data-driven view of disease biology
topic Education
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531282/
https://www.ncbi.nlm.nih.gov/pubmed/23300408
http://dx.doi.org/10.1371/journal.pcbi.1002816
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