ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

BMC Bioinformatics

Research

Alessia Auriemma Citarella¹  Luigi Di Biasi¹  Fabiola De Marco¹  Genoveffa Tortora¹ 

[1] Department of Computer Science, University of Salerno, Fisciano, Italy;

关键词: Protein classification;    Amyloidoses;    Fibrils machine learning;   

DOI  :  10.1186/s12859-022-05070-6

 received in 2022-09-07, accepted in 2022-11-21,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThis research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt–Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses.ResultsA new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset.ConclusionsThe analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.

【 授权许可】

CC BY   
© The Author(s) 2022

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