AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning

Lv, Hongwu; Yan, Ke; Guo, Yichen; Zou, Quan; Hesham, Abd El-Latif; Liu, Bin

doi:10.1016/j.compbiomed.2022.105577

Cited by 20 publications

(10 citation statements)

References 54 publications

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“…It will be important to test the predictions of this study with larger data sets, combining across databases, and more advanced machine-learning and deep-learning techniques. Specifically, future studies could explore predicting other important features of AMPs like cytotoxicity based on the feature selection, extending the current approach with ensemble methods to combine models for better prediction, and developing models to predict specific MIC values using the regression-based feature selection.…”

Section: Discussionmentioning

confidence: 99%

“…However, the major pitfall of the current study is insufficient data. It will be important to test the predictions of this study with larger data sets, combining across databases, 50 features of AMPs like cytotoxicity 51 based on the feature selection, extending the current approach with ensemble methods to combine models for better prediction, 52 and developing models to predict specific MIC values 50 using the regression-based feature selection.…”

Section: ■ Summary and Conclusionmentioning

confidence: 99%

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Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

Teimouri

Medvedeva

Kolomeisky

2023

J. Chem. Inf. Model.

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There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Summary and Conclusionmentioning

confidence: 99%

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

Teimouri

Medvedeva

Kolomeisky

2023

J. Chem. Inf. Model.

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“…AMP discovery from large-scale natural known peptide libraries is based on the antimicrobial activity prediction from traditional ML models in a screening manner. Traditional ML techniques, such as SVM [ 92 , 93 , 94 , 95 , 96 ], discriminant analysis (DA) [ 97 ], RF [ 98 , 99 , 100 , 101 ], kNN [ 95 , 102 , 103 ], and ensemble learning [ 104 , 105 , 106 , 107 , 108 ] have been applied to discover AMPs by classification. Among these methods, SVM non-linearly transforms the original input space into a higher-dimensional feature space by means of kernel functions [ 109 , 110 ].…”

Section: Amp Prediction By Traditional Machine Learningmentioning

confidence: 99%

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

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Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

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“…Ensemble learning is a significant research direction in the field of machine learning, which makes prediction results more reliable and accurate by combining several simple learners [ 28 , 29 ]. It could greatly enhance the generalization ability of the model and decrease computational errors created by a single ML model.…”

Section: Introductionmentioning

confidence: 99%

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

Lan,

Wang,

Zhang

2024

Heliyon

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AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning

Cited by 20 publications

References 54 publications

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

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