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A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system

Issue title: Papers from the 4th International Conference on Biomedical Engineering and Biotechnology (ICBEB2015), 18-21 August 2015, Shanghai, China ``New Paradigms for Biomedical Engineering and Biotechnology''

Article type: Research Article

Authors: Lim, Jongwoo | Wang, Bohyun | Lim, Joon S.*

Affiliations: IT College, Gachon University, Seongnam, Korea

Correspondence: [*] Corresponding author: Joon S. Lim, IT College, Gachon University, Seongnam, Korea. E-mail:[email protected]

Keywords: Microarray data, feature selection, neuro network fuzzy algorithm

DOI: 10.3233/THC-161187

Journal: Technology and Health Care, vol. 24, no. s2, pp. S601-S605, 2016

Published: 13 June 2016
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Abstract

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

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