Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909579577655296 |
|---|---|
| author | Tan, Joshua Zhi En Wee, JunJie Gong, Xue Xia, Kelin |
| author_facet | Tan, Joshua Zhi En Wee, JunJie Gong, Xue Xia, Kelin |
| contents | Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, the lack of efficient featurization of peptides has become a bottleneck for these machine-learning models. In this paper, we propose a topology-enhanced machine learning model (Top-ML) for anticancer peptides prediction. Our Top-ML employs peptide topological features derived from its sequence "connection" information characterized by vector and spectral descriptors. Our Top-ML model, employing an Extra-Trees classifier, has been validated on the AntiCP 2.0 and mACPpred 2.0 benchmark datasets, achieving state-of-the-art performance or results comparable to existing deep learning models, while providing greater interpretability. Our results highlight the potential of leveraging novel topology-based featurization to accelerate the identification of anticancer peptides. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction Tan, Joshua Zhi En Wee, JunJie Gong, Xue Xia, Kelin Quantitative Methods Machine Learning General Topology Biomolecules 62P10, 92C40, 68T07, 55U10 J.3; I.2.6 Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, the lack of efficient featurization of peptides has become a bottleneck for these machine-learning models. In this paper, we propose a topology-enhanced machine learning model (Top-ML) for anticancer peptides prediction. Our Top-ML employs peptide topological features derived from its sequence "connection" information characterized by vector and spectral descriptors. Our Top-ML model, employing an Extra-Trees classifier, has been validated on the AntiCP 2.0 and mACPpred 2.0 benchmark datasets, achieving state-of-the-art performance or results comparable to existing deep learning models, while providing greater interpretability. Our results highlight the potential of leveraging novel topology-based featurization to accelerate the identification of anticancer peptides. |
| title | Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction |
| topic | Quantitative Methods Machine Learning General Topology Biomolecules 62P10, 92C40, 68T07, 55U10 J.3; I.2.6 |
| url | https://arxiv.org/abs/2407.08974 |