Providing an efficient model to predict antimicrobial peptides using artificial intelligence algorithms

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Rasani Mahin; Karami Keyvan; Nassiri Mohammadreza; Tahmorthpour Mojtabi; Sekhavati Mohammad Hadi

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Journal Paper

Paper Information

Journal: AGRICULTURAL BIOTECHNOLOGY JOURNAL Year:2024 | Volume:16 | Issue:3 Page(s): 89-110

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Information Journal Paper

Title

Providing an efficient model to predict antimicrobial peptides using artificial intelligence algorithms

Author(s)

Keywords

Machine Learning Algorithms

Antimicrobial Peptides

Area under the rock curve

Confusion Matrix

Abstract

Objective The aim of this study was to propose an efficient algorithm to predict antimicrobial peptides using artificial intelligence algorithms. Materials and methods In this study, an updated AMP and non-AMP data set including physico-chemical characteristics at the level of amino acids and protein sequence in three animal species and humans was extracted. After data exploration and data pre-processing steps, four methods Supervised learning including Decision Tree, Random Forest, Naive Bayes and SVM on the AMP dataset with 10-fold cross-validation to build models and predict the AMP label using the evaluation criteria of specificity, sensitivity, rate Accuracy, precision, recall, F1 score and area under the rock curve (AUC) were evaluated. Results In this study, using an up-to-date dataset, a machine learning model has been successfully trained to predict antimicrobial peptides. A comprehensive set of features has been subjected to feature selection to identify key features of antimicrobial peptides. After selecting the feature, among the different generated models, the model based on the RF model classifier showed the best performance with Accuracy (95 percent), Precision (96 percent), Recall (95 percent), F1 Score (95 percent). the four classification of algorithms, Random Forest algorithm and SVM are the most accurate. The Decision Tree classification algorithm had the least accuracy. Conclusions According to the obtained results, the proposed RF model has a better performance than other models for AMP prediction. This model predicted some peptides as peptides with antimicrobial properties. This predictive approach can be useful in extracting AMPs with antimicrobial properties from AMP libraries in useful clinical applications before moving on to experimental studies. On the other hand, several features in the final selection properties indicate that these features are critical determinants of peptide properties and should be considered in the development of models to predict peptide activity. The executable code is available in the attached file.

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