A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system
- Authors
- Lim, Jongwoo; Wang, Bohyun; Lim, Joon S.
- Issue Date
- 2016
- Publisher
- IOS PRESS
- Keywords
- Microarray data; feature selection; neuro network fuzzy algorithm
- Citation
- TECHNOLOGY AND HEALTH CARE, v.24, pp.S601 - S605
- Journal Title
- TECHNOLOGY AND HEALTH CARE
- Volume
- 24
- Start Page
- S601
- End Page
- S605
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/9738
- DOI
- 10.3233/THC-161187
- ISSN
- 0928-7329
- 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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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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