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A species' genetic code or genome encodes valuable evolutionary, biological,and phylogenetic information that aids in species recognition, taxonomicclassification, and understanding genetic predispositions like drug resistanceand virulence. However, the vast number of potential species poses significantchallenges in developing a general-purpose whole genome classification tool.Traditional bioinformatics tools have made notable progress but lackscalability and are computationally expensive. Machine learning-basedframeworks show promise but must address the issue of large classificationvocabularies with long-tail distributions. In this study, we propose addressingthis problem through zero-shot learning using TEPI, Taxonomy-aware Embeddingand Pseudo-Imaging. We represent each genome as pseudo-images and map them to ataxonomy-aware embedding space for reasoning and classification. This embeddingspace captures compositional and phylogenetic relationships of species,enabling predictions in extensive search spaces. We evaluate TEPI using tworigorous zero-shot settings and demonstrate its generalization capabilitiesqualitatively on curated, large-scale, publicly sourced data.

TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification