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ProphTools: general prioritization tools for heterogeneous biological networks
BACKGROUND: Networks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751048/ https://www.ncbi.nlm.nih.gov/pubmed/29186475 http://dx.doi.org/10.1093/gigascience/gix111 |
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author | Navarro, Carmen Martínez, Victor Blanco, Armando Cano, Carlos |
author_facet | Navarro, Carmen Martínez, Victor Blanco, Armando Cano, Carlos |
author_sort | Navarro, Carmen |
collection | PubMed |
description | BACKGROUND: Networks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack the flexibility to analyze user-defined data. RESULTS: We developed ProphTools, a flexible open-source command-line tool that performs prioritization on a heterogeneous network. ProphTools prioritization combines a Flow Propagation algorithm similar to a Random Walk with Restarts and a weighted propagation method. A flexible model for the representation of a heterogeneous network allows the user to define a prioritization problem involving an arbitrary number of entity types and their interconnections. Furthermore, ProphTools provides functionality to perform cross-validation tests, allowing users to select the best network configuration for a given problem. ProphTools core prioritization methodology has already been proven effective in gene-disease prioritization and drug repositioning. Here we make ProphTools available to the scientific community as flexible, open-source software and perform a new proof-of-concept case study on long noncoding RNAs (lncRNAs) to disease prioritization. CONCLUSIONS: ProphTools is robust prioritization software that provides the flexibility not present in other state-of-the-art network analysis approaches, enabling researchers to perform prioritization tasks on any user-defined heterogeneous network. Furthermore, the application to lncRNA-disease prioritization shows that ProphTools can reach the performance levels of ad hoc prioritization tools without losing its generality. |
format | Online Article Text |
id | pubmed-5751048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57510482018-01-05 ProphTools: general prioritization tools for heterogeneous biological networks Navarro, Carmen Martínez, Victor Blanco, Armando Cano, Carlos Gigascience Technical Note BACKGROUND: Networks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack the flexibility to analyze user-defined data. RESULTS: We developed ProphTools, a flexible open-source command-line tool that performs prioritization on a heterogeneous network. ProphTools prioritization combines a Flow Propagation algorithm similar to a Random Walk with Restarts and a weighted propagation method. A flexible model for the representation of a heterogeneous network allows the user to define a prioritization problem involving an arbitrary number of entity types and their interconnections. Furthermore, ProphTools provides functionality to perform cross-validation tests, allowing users to select the best network configuration for a given problem. ProphTools core prioritization methodology has already been proven effective in gene-disease prioritization and drug repositioning. Here we make ProphTools available to the scientific community as flexible, open-source software and perform a new proof-of-concept case study on long noncoding RNAs (lncRNAs) to disease prioritization. CONCLUSIONS: ProphTools is robust prioritization software that provides the flexibility not present in other state-of-the-art network analysis approaches, enabling researchers to perform prioritization tasks on any user-defined heterogeneous network. Furthermore, the application to lncRNA-disease prioritization shows that ProphTools can reach the performance levels of ad hoc prioritization tools without losing its generality. Oxford University Press 2017-11-24 /pmc/articles/PMC5751048/ /pubmed/29186475 http://dx.doi.org/10.1093/gigascience/gix111 Text en © The Author(s) 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Technical Note Navarro, Carmen Martínez, Victor Blanco, Armando Cano, Carlos ProphTools: general prioritization tools for heterogeneous biological networks |
title | ProphTools: general prioritization tools for heterogeneous biological networks |
title_full | ProphTools: general prioritization tools for heterogeneous biological networks |
title_fullStr | ProphTools: general prioritization tools for heterogeneous biological networks |
title_full_unstemmed | ProphTools: general prioritization tools for heterogeneous biological networks |
title_short | ProphTools: general prioritization tools for heterogeneous biological networks |
title_sort | prophtools: general prioritization tools for heterogeneous biological networks |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751048/ https://www.ncbi.nlm.nih.gov/pubmed/29186475 http://dx.doi.org/10.1093/gigascience/gix111 |
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