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논문 기본 정보

자료유형
학술저널
저자정보
박현아 (충북대학교) 김태욱 (한국과학기술연구원) Meijing Li (Chungbuk National University) 손호선 (충북대학교) 박정석 (한국교통대학교) 류근호 (충북대학교)
저널정보
질병관리본부 Osong Public Health and Research Persptectives Osong Public Health and Research Perspectives 제6권 제2호
발행연도
2015.4
수록면
112 - 120 (9page)

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Objectives: Predicting protein function from the proteineprotein interaction network is challenging due to its complexity and huge scale of protein interaction process along with inconsistent pattern. Previously proposed methods such as neighbor counting, network analysis, and graph pattern mining has predicted functions by calculating the rules and probability of patterns inside network. Although these methods have shown good prediction, difficulty still exists in searching several functions that are exceptional from simple rules and patterns as a result of not considering the inconsistent aspect of the interaction network. Methods: In this article, we propose a novel approach using the sequential pattern mining method with gap-constraints. To overcome the inconsistency problem, we suggest frequent functional patterns to include every possible functional sequence-including patterns for which search is limited by the structure of connection or level of neighborhood layer. We also constructed a tree-graph with the most crucial interaction information of the target protein, and generated candidate sets to assign by sequential pattern mining allowing gaps. Results: The parameters of pattern length, maximum gaps, and minimum support were given to find the best setting for the most accurate prediction. The highest accuracy rate was 0.972, which showed better results than the simple neighbor counting approach and link-based approach. Conclusion: The results comparison with other approaches has confirmed that the proposed approach could reach more function candidates that previous methods could not obtain.

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