RT Journal Article
SR Electronic
T1 Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.11.335406
DO 10.1101/2020.10.11.335406
A1 Toshimichi Ikemura
A1 Kennosuke Wada
A1 Yoshiko Wada
A1 Yuki Iwasaki
A1 Takashi Abe
YR 2020
UL http://biorxiv.org/content/early/2020/10/12/2020.10.11.335406.abstract
AB 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. While only the oligonucleotide composition was given, the obtained clusters of genomes corresponded primarily to known main clades and internal divisions in the main clades. Since the BLSOM is explainable AI, it reveals which features of the oligonucleotide composition are responsible for clade clustering. The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes.Competing Interest StatementThe authors have declared no competing interest.