Cargando…
MiningABs: mining associated biomarkers across multi-connected gene expression datasets
BACKGROUND: Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. T...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4068973/ https://www.ncbi.nlm.nih.gov/pubmed/24909518 http://dx.doi.org/10.1186/1471-2105-15-173 |
_version_ | 1782322482280136704 |
---|---|
author | Cheng, Chun-Pei DeBoever, Christopher Frazer, Kelly A Liu, Yu-Cheng Tseng, Vincent S |
author_facet | Cheng, Chun-Pei DeBoever, Christopher Frazer, Kelly A Liu, Yu-Cheng Tseng, Vincent S |
author_sort | Cheng, Chun-Pei |
collection | PubMed |
description | BACKGROUND: Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. RESULTS: We propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The similarity between paired probe sequences is quantified as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Our approach is evaluated with two sets of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma datasets. Based on an unbiased reciprocal test, we demonstrate that each gene in a group of ABs is required to maintain high cancer sample classification accuracy, and we observe that ABs are not limited to genes common to all platforms. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that our ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions. CONCLUSIONS: The proposed meta-analysis method called MiningABs is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment. Java source code, tutorial, example and related materials are available at “http://sourceforge.net/projects/miningabs/”. |
format | Online Article Text |
id | pubmed-4068973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40689732014-06-27 MiningABs: mining associated biomarkers across multi-connected gene expression datasets Cheng, Chun-Pei DeBoever, Christopher Frazer, Kelly A Liu, Yu-Cheng Tseng, Vincent S BMC Bioinformatics Methodology Article BACKGROUND: Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. RESULTS: We propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The similarity between paired probe sequences is quantified as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Our approach is evaluated with two sets of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma datasets. Based on an unbiased reciprocal test, we demonstrate that each gene in a group of ABs is required to maintain high cancer sample classification accuracy, and we observe that ABs are not limited to genes common to all platforms. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that our ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions. CONCLUSIONS: The proposed meta-analysis method called MiningABs is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment. Java source code, tutorial, example and related materials are available at “http://sourceforge.net/projects/miningabs/”. BioMed Central 2014-06-08 /pmc/articles/PMC4068973/ /pubmed/24909518 http://dx.doi.org/10.1186/1471-2105-15-173 Text en Copyright © 2014 Cheng et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 | Methodology Article Cheng, Chun-Pei DeBoever, Christopher Frazer, Kelly A Liu, Yu-Cheng Tseng, Vincent S MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title | MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title_full | MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title_fullStr | MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title_full_unstemmed | MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title_short | MiningABs: mining associated biomarkers across multi-connected gene expression datasets |
title_sort | miningabs: mining associated biomarkers across multi-connected gene expression datasets |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4068973/ https://www.ncbi.nlm.nih.gov/pubmed/24909518 http://dx.doi.org/10.1186/1471-2105-15-173 |
work_keys_str_mv | AT chengchunpei miningabsminingassociatedbiomarkersacrossmulticonnectedgeneexpressiondatasets AT deboeverchristopher miningabsminingassociatedbiomarkersacrossmulticonnectedgeneexpressiondatasets AT frazerkellya miningabsminingassociatedbiomarkersacrossmulticonnectedgeneexpressiondatasets AT liuyucheng miningabsminingassociatedbiomarkersacrossmulticonnectedgeneexpressiondatasets AT tsengvincents miningabsminingassociatedbiomarkersacrossmulticonnectedgeneexpressiondatasets |