Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes
Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes
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DOI：
10.1101/2020.10.11.335406
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
T. Ikemura;K. Wada;Y. Wada;Y. Iwasaki;Takashi Abe
中科院分区：
文献类型：
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作者：
T. Ikemura;K. Wada;Y. Wada;Y. Iwasaki;Takashi Abe
无监督AI（人工智能）可以在没有特定模型或先验知识的情况下从大数据中获取新知识，并且非常适合揭示大数据中隐藏的特征。SARS-CoV-2对公众健康构成严重威胁，描述这种快速进化的病毒的一个重要问题是阐明其基因组序列变化的各个方面。我们之前建立了无监督AI，一种BLSOM（批量学习SOM），可以同时分析500万个基因组序列。本研究将BLSOM应用于4万个SARS-CoV-2基因组的寡核苷酸组成。虽然仅给出了寡核苷酸组成，但所获得的基因组簇主要对应于已知的主要进化枝和主要进化枝中的内部分裂。由于BLSOM是可解释的AI，它揭示了寡核苷酸组成的哪些特征是进化枝聚类的原因。BLSOM具有强大的图像显示能力，能够有效地发现有关病毒进化过程的知识。
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.