GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data
Background Identifying the genes responsible for diseases requires precise prioritization of significant genes. Gene expression analysis enables differentiation between gene expressions in disease and normal samples. Increasing the number of high-quality samples enhances the strength of evidence reg...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1686.pdf |
| _version_ | 1797608488284717056 |
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| author | Panisa Janyasupab Apichat Suratanee Kitiporn Plaimas |
| author_facet | Panisa Janyasupab Apichat Suratanee Kitiporn Plaimas |
| author_sort | Panisa Janyasupab |
| collection | DOAJ |
| description | Background Identifying the genes responsible for diseases requires precise prioritization of significant genes. Gene expression analysis enables differentiation between gene expressions in disease and normal samples. Increasing the number of high-quality samples enhances the strength of evidence regarding gene involvement in diseases. This process has led to the discovery of disease biomarkers through the collection of diverse gene expression data. Methods This study presents GeneCompete, a web-based tool that integrates gene expression data from multiple platforms and experiments to identify the most promising biomarkers. GeneCompete incorporates a novel union strategy and eight well-established ranking methods, including Win-Loss, Massey, Colley, Keener, Elo, Markov, PageRank, and Bi-directional PageRank algorithms, to prioritize genes across multiple gene expression datasets. Each gene in the competition is assigned a score based on log-fold change values, and significant genes are determined as winners. Results We tested the tool on the expression datasets of Hypertrophic cardiomyopathy (HCM) and the datasets from Microarray Quality Control (MAQC) project, which include both microarray and RNA-Sequencing techniques. The results demonstrate that all ranking scores have more power to predict new occurrence datasets than the classical method. Moreover, the PageRank method with a union strategy delivers the best performance for both up-regulated and down-regulated genes. Furthermore, the top-ranking genes exhibit a strong association with the disease. For MAQC, the two-sides ranking score shows a high relationship with TaqMan validation set in all log-fold change thresholds. Conclusion GeneCompete is a powerful web-based tool that revolutionizes the identification of disease-causing genes through the integration of gene expression data from multiple platforms and experiments. |
| first_indexed | 2024-03-11T05:45:05Z |
| format | Article |
| id | doaj.art-bbf26ad135854c6d865db43e0174479b |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-11T05:45:05Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-bbf26ad135854c6d865db43e0174479b2023-11-17T15:05:23ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e168610.7717/peerj-cs.1686GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression dataPanisa Janyasupab0Apichat Suratanee1Kitiporn Plaimas2Department of Mathematics and Computer Science/Faculty of Science, Chulalongkorn University, Bangkok, ThailandDepartment of Mathematics/Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Mathematics and Computer Science/Faculty of Science, Chulalongkorn University, Bangkok, ThailandBackground Identifying the genes responsible for diseases requires precise prioritization of significant genes. Gene expression analysis enables differentiation between gene expressions in disease and normal samples. Increasing the number of high-quality samples enhances the strength of evidence regarding gene involvement in diseases. This process has led to the discovery of disease biomarkers through the collection of diverse gene expression data. Methods This study presents GeneCompete, a web-based tool that integrates gene expression data from multiple platforms and experiments to identify the most promising biomarkers. GeneCompete incorporates a novel union strategy and eight well-established ranking methods, including Win-Loss, Massey, Colley, Keener, Elo, Markov, PageRank, and Bi-directional PageRank algorithms, to prioritize genes across multiple gene expression datasets. Each gene in the competition is assigned a score based on log-fold change values, and significant genes are determined as winners. Results We tested the tool on the expression datasets of Hypertrophic cardiomyopathy (HCM) and the datasets from Microarray Quality Control (MAQC) project, which include both microarray and RNA-Sequencing techniques. The results demonstrate that all ranking scores have more power to predict new occurrence datasets than the classical method. Moreover, the PageRank method with a union strategy delivers the best performance for both up-regulated and down-regulated genes. Furthermore, the top-ranking genes exhibit a strong association with the disease. For MAQC, the two-sides ranking score shows a high relationship with TaqMan validation set in all log-fold change thresholds. Conclusion GeneCompete is a powerful web-based tool that revolutionizes the identification of disease-causing genes through the integration of gene expression data from multiple platforms and experiments.https://peerj.com/articles/cs-1686.pdfBioinformaticsRanking methodMultiple gene expression dataIntegrative methodBiomarkerComputational biology |
| spellingShingle | Panisa Janyasupab Apichat Suratanee Kitiporn Plaimas GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data PeerJ Computer Science Bioinformatics Ranking method Multiple gene expression data Integrative method Biomarker Computational biology |
| title | GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| title_full | GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| title_fullStr | GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| title_full_unstemmed | GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| title_short | GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| title_sort | genecompete an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data |
| topic | Bioinformatics Ranking method Multiple gene expression data Integrative method Biomarker Computational biology |
| url | https://peerj.com/articles/cs-1686.pdf |
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