Selection of potential cancer biomarkers based on feature selection method

Chenyu Ge

doi:10.1117/12.2655891

12 January 2023 Selection of potential cancer biomarkers based on feature selection method

Chenyu Ge

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 1250910 (2023) https://doi.org/10.1117/12.2655891
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Plentiful information is contained in the massive gene expression data obtained by DNA microarray technology. Efficient computer analysis methods are beneficial for extracting helpful information quickly and accurately and assist in understanding biological phenomena from enormous data. In this paper, through the integration of fisher-score in filter layer, recursive feature elimination and logistic regression in embedded layer, FSRL is proposed to search latent biomarkers in various cancers with high mortality rates. Compared with the currently popular online analysis tool GEO2R, FSRL has higher classification accuracy. FSRL performs better in evaluation index than five methods and dramatically enhances calculative efficiency. In prostate datasets, evaluation indicator such as accuracy of FSRL, is 40.1% higher than the average. Since biomarkers obtained through multiple cancer sets are more reliable and repeatable than single analysis, cluster analysis is conducted on six cancers with high mortality rates, and 13 genes are screened to form potential genomic biomarker modules. The genes are validated through literature review, GO analysis, and functional pathway retrieval to provide information on carcinogenic mechanisms. They can be used as a decision support system for potential biomarkers to help to narrow scope of biotic experiment scope and detect multiple cancers in targeted anti-cancer therapies.

Citation Download Citation

Chenyu Ge "Selection of potential cancer biomarkers based on feature selection method", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 1250910 (12 January 2023); https://doi.org/10.1117/12.2655891

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