DeepForest-HTP: A novel deep forest approach for predicting antihypertensive peptides
Year: 2024
Type: article
Abstract: • Novel Deep Forest model improves antihypertensive peptide (AHTP) prediction. • Multi-grained cascade forest enhances AHTP classification accuracy and robustness. • Workflow includes data prep, RGB image generation, matrix conversion, and training. • Achieved 0.960 accuracy, 0.932 sensitivity, 0.989 precision, and 0.989 AUC on benchmark. • Strong generalizability shown on independent dataset, supporting real-world application. Hypertension is a major preventable risk factor for cardiovascular disease, affecting over 1.5 billion adults worldwide. Antihypertensive peptides (AHTPs) have gained attention as a natural therapeutic option with minimal side effects. This study proposes a Deep Forest-based machine learning framework for AHTP prediction, leveraging a multi-granularity cascade structure to enhance classification accuracy. We integrated data from BIOPEP-UWM and three previously used datasets, totaling 2000 peptide sequences, and introduced novel feature extraction methods to build a comprehensive dataset for model training. This study represents the first application of Deep Forest for AHTP identification, demonstrating substantial classification performance advantages over traditional methods (e.g., SVM, CNN, and XGBoost) as well as recent mainstream prediction models (Ensemble-AHTPpred, CNN-SVM Ensemble, and mAHTPred). Requiring no complex manual feature engineering, the model adapts flexibly to various data needs, offering a novel perspective for efficient AHTP prediction and promising utility in hypertension management. On the benchmark dataset, the model achieved high accuracy, sensitivity, and AUC, providing a robust tool for identifying safe and effective AHTPs. However, future efforts should incorporate larger and more diverse independent validation datasets to further improve the model and enhance its generalizability. Additionally, the model's predictive accuracy relies on known AHTP targets and sequence features, potentially limiting its ability to detect AHTPs with uncharacterized or atypical properties.
Authors: Qiyuan Bai, Hao Chen, Wenshuo Li, Lei Li, Junhao Li, Zhen Gao, Yuan Li, Xuhua Li, Bing Song
Institutions: Lanzhou University, Chinese University of Hong Kong, Chinese University of Hong Kong, Shenzhen, Zhengzhou University, First Affiliated Hospital of Zhengzhou University, Zhejiang University, Capital Medical University, Beijing Anzhen Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Fu Wai Hospital, First Hospital of Lanzhou University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,880
OpenAlex ID: W4404298235