Multi-stage feature selection in identifying potential biomarkers for cancer classification

Wong, Yit Khee and Chan, Weng Howe and Nies, Hui Wen and Moorthy, Kohbalan (2022) Multi-stage feature selection in identifying potential biomarkers for cancer classification. In: 2022 2nd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2022 , 15-16 December 2022 , Virtual, Online. pp. 6-11. (186617). ISBN 979-835039913-4

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Abstract

Biomarkers are indicators that show the disease state or its progression of certain health conditions. Identification of biomarkers greatly raises the probability of earlier diagnosis and could be further applied in developing effective treatment for the disease. Besides conducting laboratory analysis, potential biomarkers also can be identified by analysing gene expression data through feature selection and machine learning. Many algorithms have been applied and introduced in this area, yet the challenge of high dimensionality of gene expression data remains and it could lead to the existence of noise that could negatively impact the analysis outcome. Therefore, this study aims to investigate and develop a better feature selection to identify potential biomarkers from gene expression data and construct a deep neural network classification model using these selected features. Thus, a multistage feature selection, namely CIR is proposed, that composed of Chi-square, Information Gain and Recursive Feature Elimination. The dataset used in this study consists of the integration of seven ovarian cancer gene expression datasets from GEO database. Both selected genes and classification model are evaluated through biological context verification and classification performance respectively. The proposed method shows improvements over the existing methods in terms of accuracy (+2.2294%), precision (+8.1415%), recall (+2.2294%), Fl-score (+4.5494%) and AUC scores (+0.2302). The proposed CIR method successfully identified eight genes that could be potential biomarkers for ovarian cancer, including WFDC2,S100A13, PRG4, NRCAM, OGN, B3GALT2, VGLL3, and GATM which are further verified through literature.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Bioinformatics; Deep neural network; Feature selection; Gene expression
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	14 Nov 2023 03:49
Last Modified:	14 Nov 2023 03:49
URI:	http://umpir.ump.edu.my/id/eprint/39085
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