Performance Research of Clustering Methods for Detecting State Transition Trajectories in Hemoglobin

• TAKAMI Kei

  Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

• KITAMURA Yukichi

  Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan Department of Applied Chemistry and Biochemical Engineering, Faculty of Engineering, Shizuoka University, 3-5-1 Johoku Naka-ku Hamamatsu 432-8561, Japan

• NAGAOKA Masataka

  Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan Future Value Creation Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Abstract

The time-series clustering method is one of unsupervised machine learning techniques that classify time-series data. In this article, we applied three methods to the clustering analysis for 200 molecular dynamics (MD) trajectories of human adult hemoglobin (HbA), and have reported their clustering performances for detecting the T-R state transition trajectories (Traj_T-R). By compared with their silhouette indices, we have discussed the proper clustering conditions.

Journal

• Journal of Computer Chemistry, Japan

  Journal of Computer Chemistry, Japan 19 (4), 154-157, 2020

  Society of Computer Chemistry, Japan

Keywords

• Keyword Time-series clustering method
• Dynamic time warping distance
• Silhouette analysis
• Hemoglobin
• Allostery regulation

Details

• CRID: 1390850623807225088
• NII Article ID: 130008024188
• DOI: 10.2477/jccj.2021-0014
• ISSN: 13473824
• ISSN: 13471767
• Web Site: https://www.jstage.jst.go.jp/article/jccj/19/4/19_2021-0014/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed