Evaluation of miRNA-based classifiers for cancer diagnosis

Cancers account for the major deadliest noncommunicable diseases across all segments of the population and responsible for around 13% of all deaths world-wide. Cancer prevalence rate has noticeably quickened its pace in Malaysia and the world as we know it. Conventional diagnostic imaging and invasi...

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Main Authors:	Razak, Eliza, Yusof, Faridah, Ahmad Raus, Raha
Format:	Conference or Workshop Item
Language:	English English
Published:	2017
Subjects:	TA164 Bioengineering
Online Access:	http://irep.iium.edu.my/56372/ http://irep.iium.edu.my/56372/ http://irep.iium.edu.my/56372/19/56372-abstract%20book.pdf http://irep.iium.edu.my/56372/3/ICETAS.pdf

id	iium-56372
recordtype	eprints
spelling	iium-563722017-12-15T06:02:40Z http://irep.iium.edu.my/56372/ Evaluation of miRNA-based classifiers for cancer diagnosis Razak, Eliza Yusof, Faridah Ahmad Raus, Raha TA164 Bioengineering Cancers account for the major deadliest noncommunicable diseases across all segments of the population and responsible for around 13% of all deaths world-wide. Cancer prevalence rate has noticeably quickened its pace in Malaysia and the world as we know it. Conventional diagnostic imaging and invasive biopsy examinations are still the gold standard for the diagnosis of cancer. However, these conventional methods suffer from low diagnosis sensitivity compounded by work-intensive analysis. There have indeed been a number of miRNA studies to tackle the challenges associated with cancer biomarker discovery. However, the existing diagnosis techniques using miRNA suffer from low diagnosis accuracy, sensitivity, and specificity. The low diagnosis accuracy and sensitivity of the existing techniques stems from the fact that there is extremely low miRNA count in body fluids and the presence of a huge number of irrelevant miRNAs in the expression data. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper describes the state-of-the-art miRNA-based classifiers for cancer miRNA expression classification. To lower the computational complexity, we employ a heuristic-based miRNA selection approach to select relevant miRNAs that are directly responsible for cancer diagnosis. Among the classifiers, Random Forest (RF) has achieved an average accuracy of 97% over 11 independent datasets. The experimental results are quite encouraging and the predictive framework managed to classify cancer accurately even with much noise contaminated in the datasets. 2017 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/56372/19/56372-abstract%20book.pdf application/pdf en http://irep.iium.edu.my/56372/3/ICETAS.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2017) Evaluation of miRNA-based classifiers for cancer diagnosis. In: International Conference on Engineering, Technologies and Applied Sciences (ICETAS-2017), 23rd-25th January 2017, Kuala Lumpur. (Unpublished) http://icetas.etssm.org/wp-content/uploads/2017/03/booklet-ICETAS-2017-v6.pdf
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 Evaluation of miRNA-based classifiers for cancer diagnosis
description	Cancers account for the major deadliest noncommunicable diseases across all segments of the population and responsible for around 13% of all deaths world-wide. Cancer prevalence rate has noticeably quickened its pace in Malaysia and the world as we know it. Conventional diagnostic imaging and invasive biopsy examinations are still the gold standard for the diagnosis of cancer. However, these conventional methods suffer from low diagnosis sensitivity compounded by work-intensive analysis. There have indeed been a number of miRNA studies to tackle the challenges associated with cancer biomarker discovery. However, the existing diagnosis techniques using miRNA suffer from low diagnosis accuracy, sensitivity, and specificity. The low diagnosis accuracy and sensitivity of the existing techniques stems from the fact that there is extremely low miRNA count in body fluids and the presence of a huge number of irrelevant miRNAs in the expression data. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper describes the state-of-the-art miRNA-based classifiers for cancer miRNA expression classification. To lower the computational complexity, we employ a heuristic-based miRNA selection approach to select relevant miRNAs that are directly responsible for cancer diagnosis. Among the classifiers, Random Forest (RF) has achieved an average accuracy of 97% over 11 independent datasets. The experimental results are quite encouraging and the predictive framework managed to classify cancer accurately even with much noise contaminated in the datasets.
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	Evaluation of miRNA-based classifiers for cancer diagnosis
title_short	Evaluation of miRNA-based classifiers for cancer diagnosis
title_full	Evaluation of miRNA-based classifiers for cancer diagnosis
title_fullStr	Evaluation of miRNA-based classifiers for cancer diagnosis
title_full_unstemmed	Evaluation of miRNA-based classifiers for cancer diagnosis
title_sort	evaluation of mirna-based classifiers for cancer diagnosis
publishDate	2017
url	http://irep.iium.edu.my/56372/ http://irep.iium.edu.my/56372/ http://irep.iium.edu.my/56372/19/56372-abstract%20book.pdf http://irep.iium.edu.my/56372/3/ICETAS.pdf
first_indexed	2023-09-18T21:19:30Z
last_indexed	2023-09-18T21:19:30Z
_version_	1777411787032887296

Evaluation of miRNA-based classifiers for cancer diagnosis

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