A species' genetic code or genome encodes valuable evolutionary, biological,
and phylogenetic information that aids in species recognition, taxonomic
classification, and understanding genetic predispositions like drug resistance
and virulence. However, the vast number of potential species poses significant
challenges in developing a general-purpose whole genome classification tool.
Traditional bioinformatics tools have made notable progress but lack
scalability and are computationally expensive. Machine learning-based
frameworks show promise but must address the issue of large classification
vocabularies with long-tail distributions. In this study, we propose addressing
this problem through zero-shot learning using TEPI, Taxonomy-aware Embedding
and Pseudo-Imaging. We represent each genome as pseudo-images and map them to a
taxonomy-aware embedding space for reasoning and classification. This embedding
space captures compositional and phylogenetic relationships of species,
enabling predictions in extensive search spaces. We evaluate TEPI using two
rigorous zero-shot settings and demonstrate its generalization capabilities
qualitatively on curated, large-scale, publicly sourced data.

使用 TEPI（面向分类法的嵌入和伪成像）通过零样本学习，将基因组表示为伪图像并映射到具有分类法意识的嵌入空间，从而捕获物种的组成和系统发育关系，进而在广泛的搜索空间中进行预测，并通过在大规模公开数据上进行定性评估展示其广义性能。