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Evaluation of miRNA-based classifiers for cancer diagnosis

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 (Unpublished)

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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.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 4734/56372
Uncontrolled Keywords: miRNA, Cancer diagnosis, Marker selection, miRNA-based classifiers
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Biotechnology Engineering
Depositing User: DR FARIDAH YUSOF
Date Deposited: 28 Apr 2017 09:01
Last Modified: 15 Dec 2017 14:02
URI: http://irep.iium.edu.my/id/eprint/56372

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