[Published online Journal of Computer Chemistry, Japan Vol.19, -, by J-STAGE]
<Title:> Performance Research of Clustering Methods for Detecting State Transition Trajectories in Hemoglobin
<Author(s):> Kei TAKAMI, Yukichi KITAMURA, Masataka NAGAOKA
<Corresponding author E-Mill:> kitamura.yuhkichi(at)shizuoka.ac.jp
<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 (TrajT-R). By compared with their silhouette indices, we have discussed the proper clustering conditions.
<Keywords:> Keyword Time-series clustering method, Dynamic time warping distance, Silhouette analysis, Hemoglobin, Allostery regulation
<URL:> https://www.jstage.jst.go.jp/article/jccj/19/4/19_2021-0014/_article/-char/ja/
<Title:> Performance Research of Clustering Methods for Detecting State Transition Trajectories in Hemoglobin
<Author(s):> Kei TAKAMI, Yukichi KITAMURA, Masataka NAGAOKA
<Corresponding author E-Mill:> kitamura.yuhkichi(at)shizuoka.ac.jp
<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 (TrajT-R). By compared with their silhouette indices, we have discussed the proper clustering conditions.
<Keywords:> Keyword Time-series clustering method, Dynamic time warping distance, Silhouette analysis, Hemoglobin, Allostery regulation
<URL:> https://www.jstage.jst.go.jp/article/jccj/19/4/19_2021-0014/_article/-char/ja/
