RFAmyloid: A Web Server for Predicting Amyloid Proteins

• Main Authors: Mengting Niu, Yanjuan Li, Chunyu Wang, Ke Han
• Format: Article
• Language: English
• Published: MDPI AG 2018-07-01
• Series: International Journal of Molecular Sciences
• Subjects: amyloid protein; random forest; RFAmy; protein classification; machine learning
• Online Access: http://www.mdpi.com/1422-0067/19/7/2071

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.