TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification
This addresses the challenge of scalable and computationally efficient species classification for bioinformatics, though it appears incremental as it builds on zero-shot learning with a novel embedding approach.
The authors tackled the problem of whole genome classification for a vast number of species with scarce labeled data by proposing TEPI, a zero-shot learning method that represents genomes as pseudo-images and maps them to a taxonomy-aware embedding space, demonstrating generalization capabilities on curated, large-scale data.
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.