arXiv:2606.06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is encoded along two complementary axes: a $p$-adic distance on $k$-mer prefixes, which captures hierarchical positional structure, and a compositional $L_1$ distance on $k$-mer frequencies, which captures local sequence content. The two distances jointly parameterise a bi-filtered Vietoris--Rips complex, and per-sequence topological summaries from this bi-
The convergence of advanced topological data analysis with the increasing complexity of genomic data necessitates novel computational approaches for robust classification.
This development offers a new, potentially more accurate, and alignment-free method for genomic sequence classification, which could accelerate research and applications in biotechnology and medicine.
Traditional sequence alignment methods may be complemented or replaced by topological machine learning frameworks, offering new avenues for understanding genomic structure and function.
- · Biotechnology sector
- · Genomic research institutions
- · Machine learning researchers
- · AI-driven drug discovery
- · Companies reliant solely on traditional alignment algorithms
Improved classification of pathogens and genetic diseases through enhanced genomic analysis.
Faster development of targeted therapies and diagnostic tools based on deeper insights into genomic data.
The establishment of new computational paradigms for biological sequence analysis, influencing future bioinformatics tool development.
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