ArticleComputational and mathematical methods in medicine2020
PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Experimentally validated deep learning control of protein aggregation.Communications chemistry · 2026Article
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma.Frontiers in oncology · 2024Article
- Article
- Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.International journal of molecular sciences · 2023Review
- ENTAIL: yEt aNoTher amyloid fIbrils cLassifier.BMC bioinformatics · 2022Article
- AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.Scientific reports · 2022Article
- ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition.BMC bioinformatics · 2021Article
- Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D.Frontiers in genetics · 2021Article
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4 authors.
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Abstract
Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.
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