Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction
- URL: http://arxiv.org/abs/2407.08974v1
- Date: Fri, 12 Jul 2024 04:04:54 GMT
- Title: Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction
- Authors: Joshua Zhi En Tan, JunJie Wee, Xue Gong, Kelin Xia,
- Abstract summary: We propose a topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction.
Our Top-ML model has been validated on two widely used AntiCP 2.0 benchmark datasets and has achieved state-of-the-art performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 peptide prediction. Our Top-ML employs peptide topological features derived from its sequence "connection" information characterized by vector and spectral descriptors. Our Top-ML model has been validated on two widely used AntiCP 2.0 benchmark datasets and has achieved state-of-the-art performance. Our results highlight the potential of leveraging novel topology-based featurization to accelerate the identification of anticancer peptides.
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