CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.

ABSTRACT:

Background

Traditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment.

Results

This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our kn

SUBMITTER: Alipour F 

PROVIDER: S-EPMC11657719 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature