Cancer relapse prediction from microrna expression data using machine learning
Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, can...
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| Format: | Article |
| Language: | English English |
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Institute of Mechanics of Continua and Mathematical Sciences
2019
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| Online Access: | http://irep.iium.edu.my/64230/ http://irep.iium.edu.my/64230/13/64230%20CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA%20EXPRESSION%20DATA.pdf http://irep.iium.edu.my/64230/19/64230_Cancer%20Relapse%20Prediction%20from%20Microrna_wos.pdf |
| _version_ | 1848786172180955136 |
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| author | Razak, Eliza Yusof, Faridah Ahmad Raus, Raha |
| author_facet | Razak, Eliza Yusof, Faridah Ahmad Raus, Raha |
| author_sort | Razak, Eliza |
| building | IIUM Repository |
| collection | Online Access |
| description | Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework. |
| first_indexed | 2025-11-14T17:04:48Z |
| format | Article |
| id | iium-64230 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:04:48Z |
| publishDate | 2019 |
| publisher | Institute of Mechanics of Continua and Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-642302020-04-23T02:27:28Z http://irep.iium.edu.my/64230/ Cancer relapse prediction from microrna expression data using machine learning Razak, Eliza Yusof, Faridah Ahmad Raus, Raha TA164 Bioengineering Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework. Institute of Mechanics of Continua and Mathematical Sciences 2019-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64230/13/64230%20CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA%20EXPRESSION%20DATA.pdf application/pdf en http://irep.iium.edu.my/64230/19/64230_Cancer%20Relapse%20Prediction%20from%20Microrna_wos.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2019) Cancer relapse prediction from microrna expression data using machine learning. Journal of Mechanics of Continua and Mathematical Sciences, Special Issue (1). pp. 365-373. ISSN 0973-8975 E-ISSN 2454-7190 http://www.journalimcms.org/wp-content/uploads/35-Cancer-Relapse-Prediction.pdf |
| spellingShingle | TA164 Bioengineering Razak, Eliza Yusof, Faridah Ahmad Raus, Raha Cancer relapse prediction from microrna expression data using machine learning |
| title | Cancer relapse prediction from microrna expression data using machine learning |
| title_full | Cancer relapse prediction from microrna expression data using machine learning |
| title_fullStr | Cancer relapse prediction from microrna expression data using machine learning |
| title_full_unstemmed | Cancer relapse prediction from microrna expression data using machine learning |
| title_short | Cancer relapse prediction from microrna expression data using machine learning |
| title_sort | cancer relapse prediction from microrna expression data using machine learning |
| topic | TA164 Bioengineering |
| url | http://irep.iium.edu.my/64230/ http://irep.iium.edu.my/64230/ http://irep.iium.edu.my/64230/13/64230%20CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA%20EXPRESSION%20DATA.pdf http://irep.iium.edu.my/64230/19/64230_Cancer%20Relapse%20Prediction%20from%20Microrna_wos.pdf |