Efficient mining of multilevel gene association rules from microarray and gene ontology
Abstract Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tseng, Vincent S. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Information systems frontiers - Springer US, 1999, 11(2009), 4 vom: 03. März, Seite 433-447 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2009 ; number:4 ; day:03 ; month:03 ; pages:433-447 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10796-009-9156-1 |
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OLC2034126645 |
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| 520 | |a Abstract Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules. | ||
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Efficient mining of multilevel gene association rules from microarray and gene ontology |
| abstract |
Abstract Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules. © Springer Science+Business Media, LLC 2009 |
| abstractGer |
Abstract Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules. © Springer Science+Business Media, LLC 2009 |
| abstract_unstemmed |
Abstract Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules. © Springer Science+Business Media, LLC 2009 |
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| title_short |
Efficient mining of multilevel gene association rules from microarray and gene ontology |
| url |
https://doi.org/10.1007/s10796-009-9156-1 |
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| author2 |
Yu, Hsieh-Hui Yang, Shih-Chiang |
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Yu, Hsieh-Hui Yang, Shih-Chiang |
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| doi_str |
10.1007/s10796-009-9156-1 |
| up_date |
2024-07-03T19:43:00.660Z |
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7.402815 |