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14 pages, 887 KiB

Open AccessArticle

ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides

by Lantian Yao, Yuxuan Pang, Jingting Wan, Chia-Ru Chung, Jinhan Yu, Jiahui Guan, Clement Leung, Ying-Chih Chiang and Tzong-Yi Lee

Appl. Sci. 2023, 13(12), 6965; https://doi.org/10.3390/app13126965 - 9 Jun 2023

Cited by 7 | Viewed by 2097

Abstract

The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods in Healthcare and Clinical Decision Making)

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15 pages, 557 KiB

Open AccessArticle

A Heart Disease Prediction Model Based on Feature Optimization and Smote-Xgboost Algorithm

by Jian Yang and Jinhan Guan

Information 2022, 13(10), 475; https://doi.org/10.3390/info13100475 - 2 Oct 2022

Cited by 46 | Viewed by 10987

Abstract

In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine learning are designed to learn and predict the disease status of patients from a large amount of pathological data. Practice has proved that such a system has the potential to save more lives. Therefore, this paper proposes a new framework for predicting heart disease using the smote-xgboost algorithm. First, we propose a feature selection method based on information gain, which aims to extract key features from the dataset and prevent model overfitting. Second, we use the Smote-Enn algorithm to process unbalanced data, and obtain sample data with roughly the same positive and negative categories. Finally, we test the prediction effect of Xgboost algorithm and five other baseline algorithms on sample data. The results show that our proposed method achieves the best performance in the five indicators of accuracy, precision, recall, F1-score and AUC, and the framework proposed in this paper has significant advantages in heart disease prediction. Full article

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