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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.16 No.1
- 발행연도
- 2017.3
- 수록면
- 64 - 79 (16page)
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초록· 키워드
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Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.
목차
ABSTRACT
1. INTRODUCTION
2. EXISTING METHODS
3. STEPWISE RANDOM FORESTS
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCES
참고문헌 (73)
참고문헌 신청
Adie, E., Adams, R., Evans, K., Porteous, D., and Pickard, B. (2005), Speeding disease gene discovery by sequence based candidate prioritization, BMC bioinformatics, 6-55.
Aerts, S., Lambrechts, D., Maity, S., Van Loo, P., Coessens, B., De Smet, F., Tranchevent, L., De Moor, B., Marynen, P., and Hassan, B. (2006), Gene prioritization through genomic data fusion, Nat Biotechnol, 24, 537-544.
Blum, A. and Langley, P. (1997), Selection of relevant features and examples in machine learning, Artificial Intelligence, 97(1-2), 245-271.
Bollmann, P. and Cherniavsky, V. S. (1981), Restricted Evaluation in Information Retrieval, ACM SIGIR.
Botstein, D. and Risch, N. (2013), Discovering genotypes underlying human phenotypes: Past successes for mendelian disease, future approaches for complex disease, Nature Genetics, 33, 228-237.
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UCI(KEPA) : I410-ECN-0101-2017-530-002373422