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. 2022 Dec 1;23(1):517.
doi: 10.1186/s12859-022-05070-6.

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

Alessia Auriemma Citarella  1 , Luigi Di Biasi  2 , Fabiola De Marco  2 , Genoveffa Tortora  2
Affiliations

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

Alessia Auriemma Citarella et al. BMC Bioinformatics. .

Abstract

Background: This 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.

Results: A 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.

Conclusions: The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.

Keywords: Amyloidoses; Fibrils machine learning; Protein classification.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The composition of the aggregated dataset based on the length of the sequences
Fig. 2
Fig. 2
Comparison of the experiments and their configuration in the test phase
Fig. 3
Fig. 3
Confusion matrices for the best experiments. From left to right: experiment 2, experiment 5, and experiment 8
Fig. 4
Fig. 4
Roc Curves for the best experiments. From left to right: experiment 2, experiment 5, and experiment 8
See this image and copyright information in PMC

References

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