Cancer relapse prediction from microrna expression data using machine learning

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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Main Authors: Razak, Eliza, Yusof, Faridah, Ahmad Raus, Raha
Format: Conference or Workshop Item
Language:English
English
Published: 2018
Subjects:
TA164 Bioengineering
Online Access:http://irep.iium.edu.my/64229/
http://irep.iium.edu.my/64229/
http://irep.iium.edu.my/64229/1/64229_CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA.pdf
http://irep.iium.edu.my/64229/2/64229_CANCER%20RELAPSE%20PREDICTION%20-%20paper.pdf
id iium-64229
recordtype eprints
spelling iium-642292018-06-28T04:23:33Z http://irep.iium.edu.my/64229/ 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. 2018 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/64229/1/64229_CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA.pdf application/pdf en http://irep.iium.edu.my/64229/2/64229_CANCER%20RELAPSE%20PREDICTION%20-%20paper.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2018) Cancer relapse prediction from microrna expression data using machine learning. In: 3rd Current Research on Information Technology, Mathematics Sciences, Science and Technology (CRIMSTIC 2018), 27th-29th April 2018, Bandar Hilir, Melaka. (Unpublished) http://www.crimstic.org/
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic TA164 Bioengineering
spellingShingle TA164 Bioengineering
Razak, Eliza
Yusof, Faridah
Ahmad Raus, Raha
Cancer relapse prediction from microrna expression data using machine learning
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.
format Conference or Workshop Item
author Razak, Eliza
Yusof, Faridah
Ahmad Raus, Raha
author_facet Razak, Eliza
Yusof, Faridah
Ahmad Raus, Raha
author_sort Razak, Eliza
title Cancer relapse prediction from microrna expression data using machine learning
title_short 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_sort cancer relapse prediction from microrna expression data using machine learning
publishDate 2018
url http://irep.iium.edu.my/64229/
http://irep.iium.edu.my/64229/
http://irep.iium.edu.my/64229/1/64229_CANCER%20RELAPSE%20PREDICTION%20FROM%20MICRORNA.pdf
http://irep.iium.edu.my/64229/2/64229_CANCER%20RELAPSE%20PREDICTION%20-%20paper.pdf
first_indexed 2023-09-18T21:31:06Z
last_indexed 2023-09-18T21:31:06Z
_version_ 1777412516835491840

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