Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes.

ABSTRACT:

Background

Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes. We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously. The present study applied the BLSOM to the oligonucleotide compositions of forty thousand SARS-CoV-2 genomes.

Results

While only the oligonucleotide composition was given, the obtained clusters of genomes corresponded primarily to known main clades and inte

SUBMITTER: Iwasaki Y 

PROVIDER: S-EPMC8907386 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature