Cargando…
Prioritizing candidate diseases-related metabolites based on literature and functional similarity
BACKGROUND: As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understand...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876110/ https://www.ncbi.nlm.nih.gov/pubmed/31760947 http://dx.doi.org/10.1186/s12859-019-3127-4 |
_version_ | 1783473157768740864 |
---|---|
author | Wang, Yongtian Juan, Liran Peng, Jiajie Zang, Tianyi Wang, Yadong |
author_facet | Wang, Yongtian Juan, Liran Peng, Jiajie Zang, Tianyi Wang, Yadong |
author_sort | Wang, Yongtian |
collection | PubMed |
description | BACKGROUND: As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases. RESULTS: We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%. CONCLUSION: In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites. |
format | Online Article Text |
id | pubmed-6876110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68761102019-11-29 Prioritizing candidate diseases-related metabolites based on literature and functional similarity Wang, Yongtian Juan, Liran Peng, Jiajie Zang, Tianyi Wang, Yadong BMC Bioinformatics Research BACKGROUND: As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases. RESULTS: We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%. CONCLUSION: In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites. BioMed Central 2019-11-25 /pmc/articles/PMC6876110/ /pubmed/31760947 http://dx.doi.org/10.1186/s12859-019-3127-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wang, Yongtian Juan, Liran Peng, Jiajie Zang, Tianyi Wang, Yadong Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title | Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title_full | Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title_fullStr | Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title_full_unstemmed | Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title_short | Prioritizing candidate diseases-related metabolites based on literature and functional similarity |
title_sort | prioritizing candidate diseases-related metabolites based on literature and functional similarity |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876110/ https://www.ncbi.nlm.nih.gov/pubmed/31760947 http://dx.doi.org/10.1186/s12859-019-3127-4 |
work_keys_str_mv | AT wangyongtian prioritizingcandidatediseasesrelatedmetabolitesbasedonliteratureandfunctionalsimilarity AT juanliran prioritizingcandidatediseasesrelatedmetabolitesbasedonliteratureandfunctionalsimilarity AT pengjiajie prioritizingcandidatediseasesrelatedmetabolitesbasedonliteratureandfunctionalsimilarity AT zangtianyi prioritizingcandidatediseasesrelatedmetabolitesbasedonliteratureandfunctionalsimilarity AT wangyadong prioritizingcandidatediseasesrelatedmetabolitesbasedonliteratureandfunctionalsimilarity |