Classification of miRNA expression data using random forests for cancer diagnosis

Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Co...

Full description

Bibliographic Details
Main Authors:	Razak, Eliza, Yusof, Faridah, Ahmad Raus, Raha
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2016
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/7/54510.pdf http://irep.iium.edu.my/54510/8/54510-Classification%20of%20miRNA%20Expression%20Data%20Using%20Random%20Forests%20for%20Cancer%20Diagnosis_SCOPUS.pdf

id	iium-54510
recordtype	eprints
spelling	iium-545102017-03-28T07:54:08Z http://irep.iium.edu.my/54510/ Classification of miRNA expression data using random forests for cancer diagnosis Razak, Eliza Yusof, Faridah Ahmad Raus, Raha T Technology (General) Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Conventional methods of diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms which usually appear in the late stage of cancer. Furthermore, biopsy examinations are highly invasive since tissue samples are required to be extracted from patients. There exist minimally invasive cancer biomarkers in forms of proteins from serum. Nevertheless, existing protein-based diagnosis techniques require labor-intensive analysis compounded by low diagnosis sensitivity. There have indeed been a number of studies to identify novel miRNA-based cancer biomarkers. 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. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper proposes to circumvent these problems in data analysis stage with a machine learning technique called Random Forest. The proposed system achieved 93.48 % accuracy for gastric cancer and 100 % accuracy for ovarian cancer. The results are promising and encouraging. Despite much noise contaminated the sample preparation process and low miRNA count in body fluids, the proposed system able to identify miRNA markers responsible for classification of cancer. IEEE 2016 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/54510/7/54510.pdf application/pdf en http://irep.iium.edu.my/54510/8/54510-Classification%20of%20miRNA%20Expression%20Data%20Using%20Random%20Forests%20for%20Cancer%20Diagnosis_SCOPUS.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2016) Classification of miRNA expression data using random forests for cancer diagnosis. In: 6th International Conference on Computer and Communication Engineering (ICCCE 2016), 25th-27th July 2016, Kuala Lumpur. http://ieeexplore.ieee.org/document/7808307/ 10.1109/ICCCE.2016.49
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Razak, Eliza Yusof, Faridah Ahmad Raus, Raha Classification of miRNA expression data using random forests for cancer diagnosis
description	Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Conventional methods of diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms which usually appear in the late stage of cancer. Furthermore, biopsy examinations are highly invasive since tissue samples are required to be extracted from patients. There exist minimally invasive cancer biomarkers in forms of proteins from serum. Nevertheless, existing protein-based diagnosis techniques require labor-intensive analysis compounded by low diagnosis sensitivity. There have indeed been a number of studies to identify novel miRNA-based cancer biomarkers. 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. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper proposes to circumvent these problems in data analysis stage with a machine learning technique called Random Forest. The proposed system achieved 93.48 % accuracy for gastric cancer and 100 % accuracy for ovarian cancer. The results are promising and encouraging. Despite much noise contaminated the sample preparation process and low miRNA count in body fluids, the proposed system able to identify miRNA markers responsible for classification of cancer.
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	Classification of miRNA expression data using random forests for cancer diagnosis
title_short	Classification of miRNA expression data using random forests for cancer diagnosis
title_full	Classification of miRNA expression data using random forests for cancer diagnosis
title_fullStr	Classification of miRNA expression data using random forests for cancer diagnosis
title_full_unstemmed	Classification of miRNA expression data using random forests for cancer diagnosis
title_sort	classification of mirna expression data using random forests for cancer diagnosis
publisher	IEEE
publishDate	2016
url	http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/ http://irep.iium.edu.my/54510/7/54510.pdf http://irep.iium.edu.my/54510/8/54510-Classification%20of%20miRNA%20Expression%20Data%20Using%20Random%20Forests%20for%20Cancer%20Diagnosis_SCOPUS.pdf
first_indexed	2023-09-18T21:17:08Z
last_indexed	2023-09-18T21:17:08Z
_version_	1777411637338177536

Classification of miRNA expression data using random forests for cancer diagnosis

Similar Items