TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916103853178880 |
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| author | Aakur, Sathyanarayanan Laguduva, Vishalini R. Ramamurthy, Priyadharsini Ramachandran, Akhilesh |
| author_facet | Aakur, Sathyanarayanan Laguduva, Vishalini R. Ramamurthy, Priyadharsini Ramachandran, Akhilesh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_13219 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification Aakur, Sathyanarayanan Laguduva, Vishalini R. Ramamurthy, Priyadharsini Ramachandran, Akhilesh Genomics Artificial Intelligence Machine Learning 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. |
| title | TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.13219 |