CAKL: Commutative algebra k-mer learning of genomics
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912535832166400 |
|---|---|
| author | Suwayyid, Faisal Hozumi, Yuta Feng, Hongsong Zia, Mushal Wee, JunJie Wei, Guo-Wei |
| author_facet | Suwayyid, Faisal Hozumi, Yuta Feng, Hongsong Zia, Mushal Wee, JunJie Wei, Guo-Wei |
| contents | Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer learning (CAKL) as the first-ever nonlinear algebraic framework for analyzing genomic sequences. CAKL bridges between commutative algebra, algebraic topology, combinatorics, and machine learning to establish a new mathematical paradigm for comparative genomic analysis. We evaluate its effectiveness on three tasks -- genetic variant identification, phylogenetic tree analysis, and viral genome classification -- typically requiring alignment-based, alignment-free, and machine-learning approaches, respectively. Across eleven datasets, CAKL outperforms five state-of-the-art sequence analysis methods, particularly in viral classification, and maintains stable predictive accuracy as dataset size increases, underscoring its scalability and robustness. This work ushers in a new era in commutative algebraic data analysis and learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CAKL: Commutative algebra k-mer learning of genomics Suwayyid, Faisal Hozumi, Yuta Feng, Hongsong Zia, Mushal Wee, JunJie Wei, Guo-Wei Quantitative Methods Commutative Algebra 13P25, 92D20 Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer learning (CAKL) as the first-ever nonlinear algebraic framework for analyzing genomic sequences. CAKL bridges between commutative algebra, algebraic topology, combinatorics, and machine learning to establish a new mathematical paradigm for comparative genomic analysis. We evaluate its effectiveness on three tasks -- genetic variant identification, phylogenetic tree analysis, and viral genome classification -- typically requiring alignment-based, alignment-free, and machine-learning approaches, respectively. Across eleven datasets, CAKL outperforms five state-of-the-art sequence analysis methods, particularly in viral classification, and maintains stable predictive accuracy as dataset size increases, underscoring its scalability and robustness. This work ushers in a new era in commutative algebraic data analysis and learning. |
| title | CAKL: Commutative algebra k-mer learning of genomics |
| topic | Quantitative Methods Commutative Algebra 13P25, 92D20 |
| url | https://arxiv.org/abs/2508.09406 |