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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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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. |
format | Online Article Text |
id | pubmed-3531282 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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