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On Crowd-verification of Biological Networks
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and ex...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798292/ https://www.ncbi.nlm.nih.gov/pubmed/24151423 http://dx.doi.org/10.4137/BBI.S12932 |
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author | Ansari, Sam Binder, Jean Boue, Stephanie Di Fabio, Anselmo Hayes, William Hoeng, Julia Iskandar, Anita Kleiman, Robin Norel, Raquel O’Neel, Bruce Peitsch, Manuel C. Poussin, Carine Pratt, Dexter Rhrissorrakrai, Kahn Schlage, Walter K. Stolovitzky, Gustavo Talikka, Marja |
author_facet | Ansari, Sam Binder, Jean Boue, Stephanie Di Fabio, Anselmo Hayes, William Hoeng, Julia Iskandar, Anita Kleiman, Robin Norel, Raquel O’Neel, Bruce Peitsch, Manuel C. Poussin, Carine Pratt, Dexter Rhrissorrakrai, Kahn Schlage, Walter K. Stolovitzky, Gustavo Talikka, Marja |
collection | PubMed |
description | Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. |
format | Online Article Text |
id | pubmed-3798292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-37982922013-10-22 On Crowd-verification of Biological Networks Ansari, Sam Binder, Jean Boue, Stephanie Di Fabio, Anselmo Hayes, William Hoeng, Julia Iskandar, Anita Kleiman, Robin Norel, Raquel O’Neel, Bruce Peitsch, Manuel C. Poussin, Carine Pratt, Dexter Rhrissorrakrai, Kahn Schlage, Walter K. Stolovitzky, Gustavo Talikka, Marja Bioinform Biol Insights Original Research Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. Libertas Academica 2013-10-10 /pmc/articles/PMC3798292/ /pubmed/24151423 http://dx.doi.org/10.4137/BBI.S12932 Text en © 2013 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article published under the Creative Commons CC-BY-NC 3.0 license. |
spellingShingle | Original Research Ansari, Sam Binder, Jean Boue, Stephanie Di Fabio, Anselmo Hayes, William Hoeng, Julia Iskandar, Anita Kleiman, Robin Norel, Raquel O’Neel, Bruce Peitsch, Manuel C. Poussin, Carine Pratt, Dexter Rhrissorrakrai, Kahn Schlage, Walter K. Stolovitzky, Gustavo Talikka, Marja On Crowd-verification of Biological Networks |
title | On Crowd-verification of Biological Networks |
title_full | On Crowd-verification of Biological Networks |
title_fullStr | On Crowd-verification of Biological Networks |
title_full_unstemmed | On Crowd-verification of Biological Networks |
title_short | On Crowd-verification of Biological Networks |
title_sort | on crowd-verification of biological networks |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798292/ https://www.ncbi.nlm.nih.gov/pubmed/24151423 http://dx.doi.org/10.4137/BBI.S12932 |
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