Informative top-k class associative rule for cancer biomarker discovery on microarray data
The discovery of reliable cancer biomarkers is crucial for accurate early detection and clinical diagnosis. One of the strategies is by identifying expression-based cancer biomarkers through integrative microarray data analysis. Microarray is a powerful high-throughput technology, which allows a gen...
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| Format: | Article |
| Language: | English |
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Elsevier
2020
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| Online Access: | http://eprints.uthm.edu.my/5262/ http://eprints.uthm.edu.my/5262/1/AJ%202020%20%28129%29.pdf |
| _version_ | 1848888507018248192 |
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| author | Ong, Huey Fang Mustapha, Norwati Hamdan, Hazlina Rosli, Rozita Mustapha, Aida |
| author_facet | Ong, Huey Fang Mustapha, Norwati Hamdan, Hazlina Rosli, Rozita Mustapha, Aida |
| author_sort | Ong, Huey Fang |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | The discovery of reliable cancer biomarkers is crucial for accurate early detection and clinical diagnosis. One of the strategies is by identifying expression-based cancer biomarkers through integrative microarray data analysis. Microarray is a powerful high-throughput technology, which allows a genome-wide analysis of human genes with various biological information. Nevertheless, more studies are needed on improving the predictability of the discovered gene biomarkers, as well as their reproducibility and interpretability, to qualify them for clinical use. This paper proposes an informative top-k class associative rule ( i TCAR) method in an integrative framework for identifying candidate genes of specific cancers. i TCAR introduces an enhanced associative classification algorithm that integrates microarray data with biological informa- tion from gene ontology, KEGG pathways, and protein-protein interactions to generate informative class associative rules. A new interestingness measurement is used to rank and select class associative rules for building accurate classifiers. The experimental results show that i TCAR has excellent predictability by achieving the average classification accuracy above 90% and the average area under the curve above 0.8. Besides, i TCAR has significant reproducibility and interpretability through functional enrichment analy- sis and retrieval of meaningful cancer terms. These promising results suggest the proposed method has great potential in identifying candidate genes, which can be further investigated as biomarkers for cancer diseases. |
| first_indexed | 2025-11-15T20:11:22Z |
| format | Article |
| id | uthm-5262 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:11:22Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
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| spelling | uthm-52622022-01-06T08:16:40Z http://eprints.uthm.edu.my/5262/ Informative top-k class associative rule for cancer biomarker discovery on microarray data Ong, Huey Fang Mustapha, Norwati Hamdan, Hazlina Rosli, Rozita Mustapha, Aida T Technology (General) T173.2-174.5 Technological change The discovery of reliable cancer biomarkers is crucial for accurate early detection and clinical diagnosis. One of the strategies is by identifying expression-based cancer biomarkers through integrative microarray data analysis. Microarray is a powerful high-throughput technology, which allows a genome-wide analysis of human genes with various biological information. Nevertheless, more studies are needed on improving the predictability of the discovered gene biomarkers, as well as their reproducibility and interpretability, to qualify them for clinical use. This paper proposes an informative top-k class associative rule ( i TCAR) method in an integrative framework for identifying candidate genes of specific cancers. i TCAR introduces an enhanced associative classification algorithm that integrates microarray data with biological informa- tion from gene ontology, KEGG pathways, and protein-protein interactions to generate informative class associative rules. A new interestingness measurement is used to rank and select class associative rules for building accurate classifiers. The experimental results show that i TCAR has excellent predictability by achieving the average classification accuracy above 90% and the average area under the curve above 0.8. Besides, i TCAR has significant reproducibility and interpretability through functional enrichment analy- sis and retrieval of meaningful cancer terms. These promising results suggest the proposed method has great potential in identifying candidate genes, which can be further investigated as biomarkers for cancer diseases. Elsevier 2020 Article PeerReviewed text en http://eprints.uthm.edu.my/5262/1/AJ%202020%20%28129%29.pdf Ong, Huey Fang and Mustapha, Norwati and Hamdan, Hazlina and Rosli, Rozita and Mustapha, Aida (2020) Informative top-k class associative rule for cancer biomarker discovery on microarray data. Expert Systems With Applications, 146. pp. 1-18. ISSN 0957-4174 https://doi.org/10.1016/j.eswa.2019.113169 |
| spellingShingle | T Technology (General) T173.2-174.5 Technological change Ong, Huey Fang Mustapha, Norwati Hamdan, Hazlina Rosli, Rozita Mustapha, Aida Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title | Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title_full | Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title_fullStr | Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title_full_unstemmed | Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title_short | Informative top-k class associative rule for cancer biomarker discovery on microarray data |
| title_sort | informative top-k class associative rule for cancer biomarker discovery on microarray data |
| topic | T Technology (General) T173.2-174.5 Technological change |
| url | http://eprints.uthm.edu.my/5262/ http://eprints.uthm.edu.my/5262/ http://eprints.uthm.edu.my/5262/1/AJ%202020%20%28129%29.pdf |