AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.

Change log
Charoenkwan, Phasit 
Ahmed, Saeed 
Nantasenamat, Chanin 
Quinn, Julian MW 
Moni, Mohammad Ali 

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at . It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.


Funder: Mahidol University

Funder: Chiang Mai University

Funder: College of Arts, Media and Technology, Chiang Mai University

Funder: Information Technology Service Center (ITSC) of Chiang Mai University

Algorithms, Amyloidogenic Proteins, Computational Biology, Diabetes Mellitus, Type 2, Humans, Machine Learning, Support Vector Machine
Journal Title
Sci Rep
Conference Name
Journal ISSN
Volume Title
Springer Science and Business Media LLC