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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Publications International Lahore, Pakistan
2017
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iium-563792017-12-15T06:03:28Z http://irep.iium.edu.my/56379/ 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. Publications International Lahore, Pakistan 2017 Article PeerReviewed application/pdf en http://irep.iium.edu.my/56379/1/56379_Evaluation%20of%20miRNA-based.pdf Razak, Eliza and Yusof, Faridah and Ahmad Raus, Raha (2017) Evaluation of miRNA-based classifiers for cancer diagnosis. Science International, 29 (5). pp. 1019-1024. ISSN 1013-5316 http://www.sci-int.com/pdf/636402295881282961.pdf |
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Digital Repository |
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Local University |
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International Islamic University Malaysia |
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IIUM Repository |
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Online Access |
| language |
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 |
Article |
| 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 |
| publisher |
Publications International Lahore, Pakistan |
| publishDate |
2017 |
| url |
http://irep.iium.edu.my/56379/ http://irep.iium.edu.my/56379/ http://irep.iium.edu.my/56379/1/56379_Evaluation%20of%20miRNA-based.pdf |
| first_indexed |
2023-09-18T21:19:31Z |
| last_indexed |
2023-09-18T21:19:31Z |
| _version_ |
1777411787617992704 |