AngClust

Citation:
Aimin Li, Siqi Xiong, Junhuai Li, Saurav Mallik, Yajun Liu, Rong Fei, Hongfang Zhou, Guangming Liu. AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles. January 2022. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM. DOI: 10.1109/TCBB.2022.3192306

Full text:
https://ieeexplore.ieee.org/document/9833353/ https://pubmed.ncbi.nlm.nih.gov/35853049/

Highlights
* We proposed a novel clustering algorithm based on angular features for short-term gene expression profiles.
* We defined three indicators to identify significant clusters: (i) the fluctuation degree of expression levels, (ii) homogeneity, and (iii) the degree of clustering while the clusters are functionally significant.
* The clustering outcome of our algorithm (AngClust) is better than the currently most popular STEM algorithm.
* AngClust can be used to analyze any short time series gene expression profiles.