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Improved network community structure improves function prediction

Cited 276 time in Web of Science Cited 34 time in Scopus

Lee, Ju Yong; Gross, Steven P.; Lee, Joo Young

Issue Date
Nature Publishing Group
Scientific Reports, Vol.3, p. 2197
We are overwhelmed by experimental data, and need better ways to understand large interaction datasets. While clustering related nodes in such networks-known as community detection-appears a promising approach, detecting such communities is computationally difficult. Further, how to best use such community information has not been determined. Here, within the context of protein function prediction, we address both issues. First, we apply a novel method that generates improved modularity solutions than the current state of the art. Second, we develop a better method to use this community information to predict proteins' functions. We discuss when and why this community information is important. Our results should be useful for two distinct scientific communities: first, those using various cost functions to detect community structure, where our new optimization approach will improve solutions, and second, those working to extract novel functional information about individual nodes from large interaction datasets.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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