  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.
