Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition

Despite numerous breakthroughs in personalized medicine, cancer still remains as a major leading cause of death globally. Cancer occurrence rate is increasing at an alarming rate in the world and it accounts for around 13% of all deaths worldwide. Having anaplastic, dysplastic, and metastatic proper...

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Main Authors:	Razak, Eliza, Yusof, Faridah, Ahmad Raus, Raha
Format:	Article
Language:	English English
Published:	RGN Publications 2018
Subjects:	TA164 Bioengineering
Online Access:	http://irep.iium.edu.my/64231/ http://irep.iium.edu.my/64231/ http://irep.iium.edu.my/64231/1/subtypes.pdf http://irep.iium.edu.my/64231/7/acceptance.pdf

id	iium-64231
recordtype	eprints
spelling	iium-642312018-07-13T02:54:15Z http://irep.iium.edu.my/64231/ Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition Razak, Eliza Yusof, Faridah Ahmad Raus, Raha TA164 Bioengineering Despite numerous breakthroughs in personalized medicine, cancer still remains as a major leading cause of death globally. Cancer occurrence rate is increasing at an alarming rate in the world and it accounts for around 13% of all deaths worldwide. Having anaplastic, dysplastic, and metastatic properties in normal cells, are the initiative signs of carcinogenesis and metastasis. Conventional diagnosis of cancer subtypes are still based on pathologic guided histopathological diagnosis. However, certain clinical signs and symptoms usually appear in late stages of cancer and hence the vast majority of patients are diagnosed with cancer very late. Therefore, it is of paramount importance to predict the type of cancer and its histopathologic subtypes in early stages so that specific treatments can be sought. Furthermore, it has been demonstrated in the medical literature that specific treatments based on histopathologic types could dramatically increase the survival rate of the patients. Nevertheless, clinicians cannot fully rely on blood-based protein biomarkers in actual clinical practice because of low sensitivity and specificity, time consuming and intensive laboratory sampling procedures, and fail to provide early cancer detection. 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 histopathologic subtypes. This paper describes a pattern recognition based approach called one-dependent estimator to predict cancer histopathologic subtypes from miRNA expression data. The proposed framework will predict particular cancer subtypes. To select relevant cancer subtypes associated miRNAs, we employ an entropy-based marker selection approach. This proposed system has achieved an average accuracy of 96.21% in predicting cancer subtypes over 4 datasets, namely pancreatic cancer, ovarian cancer, lymphoma, and renal cell carcinoma (RCC).The experimental results strongly suggest the efficacy of the entire proposed framework. RGN Publications 2018 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64231/1/subtypes.pdf application/pdf en http://irep.iium.edu.my/64231/7/acceptance.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2018) Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition. Journal of Informatics and Mathematical Sciences. ISSN 0974-875X E-ISSN 0975-5748 (In Press) http://www.rgnpublications.com/journals/index.php/jims/index
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 histopathologic subtypes prediction from miRNA expression data using pattern recognition
description	Despite numerous breakthroughs in personalized medicine, cancer still remains as a major leading cause of death globally. Cancer occurrence rate is increasing at an alarming rate in the world and it accounts for around 13% of all deaths worldwide. Having anaplastic, dysplastic, and metastatic properties in normal cells, are the initiative signs of carcinogenesis and metastasis. Conventional diagnosis of cancer subtypes are still based on pathologic guided histopathological diagnosis. However, certain clinical signs and symptoms usually appear in late stages of cancer and hence the vast majority of patients are diagnosed with cancer very late. Therefore, it is of paramount importance to predict the type of cancer and its histopathologic subtypes in early stages so that specific treatments can be sought. Furthermore, it has been demonstrated in the medical literature that specific treatments based on histopathologic types could dramatically increase the survival rate of the patients. Nevertheless, clinicians cannot fully rely on blood-based protein biomarkers in actual clinical practice because of low sensitivity and specificity, time consuming and intensive laboratory sampling procedures, and fail to provide early cancer detection. 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 histopathologic subtypes. This paper describes a pattern recognition based approach called one-dependent estimator to predict cancer histopathologic subtypes from miRNA expression data. The proposed framework will predict particular cancer subtypes. To select relevant cancer subtypes associated miRNAs, we employ an entropy-based marker selection approach. This proposed system has achieved an average accuracy of 96.21% in predicting cancer subtypes over 4 datasets, namely pancreatic cancer, ovarian cancer, lymphoma, and renal cell carcinoma (RCC).The experimental results strongly suggest the efficacy of the entire proposed framework.
format	Article
author	Razak, Eliza Yusof, Faridah Ahmad Raus, Raha
author_facet	Razak, Eliza Yusof, Faridah Ahmad Raus, Raha
author_sort	Razak, Eliza
title	Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition
title_short	Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition
title_full	Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition
title_fullStr	Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition
title_full_unstemmed	Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition
title_sort	cancer histopathologic subtypes prediction from mirna expression data using pattern recognition
publisher	RGN Publications
publishDate	2018
url	http://irep.iium.edu.my/64231/ http://irep.iium.edu.my/64231/ http://irep.iium.edu.my/64231/1/subtypes.pdf http://irep.iium.edu.my/64231/7/acceptance.pdf
first_indexed	2023-09-18T21:31:07Z
last_indexed	2023-09-18T21:31:07Z
_version_	1777412516974952448

Cancer histopathologic subtypes prediction from miRNA expression data using pattern recognition

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