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. 2024 Sep 23;25(6):bbae534.
doi: 10.1093/bib/bbae534.

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization

Wenyu Zhang  1   2 , Yijie Ding  2 , Leyi Wei  3 , Xiaoyi Guo  2 , Fengming Ni  4
Affiliations

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization

Wenyu Zhang et al. Brief Bioinform. .

Abstract

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Keywords: Laplacian regularized model; kernel risk-sensitive mean p-power error; multi-information fusion; sequence classification; therapeutic peptides.

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Figures

Figure 1
Figure 1
Process of the feature extraction.
Figure 2
Figure 2
Kernel function curve of KRP with different parameters.
Figure 3
Figure 3
The derivative of KRP with different parameters, as well as the derivative of the MSE [35].
Figure 4
Figure 4
Framework of the proposed approach.
Figure 5
Figure 5
Feature distribution.
Figure 6
Figure 6
The ROC curves and AUC values of various frameworks on training datasets.
Figure 7
Figure 7
The ROC curves and AUC values of various frameworks on an independent testing set.
Figure 8
Figure 8
ACC and MCC of different models on the training datasets and independent datasets.
See this image and copyright information in PMC

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