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Related Experiment Video

Updated: Aug 19, 2025

Use of Two Dimensional Semi-denaturing Detergent Agarose Gel Electrophoresis to Confirm Size Heterogeneity of Amyloid or Amyloid-like Fibers

Use of Two Dimensional Semi-denaturing Detergent Agarose Gel Electrophoresis to Confirm Size Heterogeneity of Amyloid or Amyloid-like Fibers

Published on: April 26, 2018

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier.

Alessia Auriemma Citarella¹, Luigi Di Biasi², Fabiola De Marco²

¹Department of Computer Science, University of Salerno, Fisciano, Italy. aauriemmacitarella@unisa.it.

BMC Bioinformatics

|December 1, 2022

Summary

This summary is machine-generated.

A new classifier, ENTAIL, aids in identifying amyloid fibril precursors. This tool, using over 4000 molecular descriptors, achieved 81.80% accuracy on a balanced dataset for improved amyloidosis research.

Keywords:

Amyloidoses Fibrils machine learning Protein classification

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Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

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Last Updated: Aug 19, 2025

Use of Two Dimensional Semi-denaturing Detergent Agarose Gel Electrophoresis to Confirm Size Heterogeneity of Amyloid or Amyloid-like Fibers

Use of Two Dimensional Semi-denaturing Detergent Agarose Gel Electrophoresis to Confirm Size Heterogeneity of Amyloid or Amyloid-like Fibers

Published on: April 26, 2018

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Published on: October 20, 2017

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Area of Science:

Biochemistry
Computational Biology
Medical Research

Background:

Amyloidoses are protein misfolding disorders impacting protein function and structure.
Fibrillar deposits characterize diseases like Alzheimer's, Creutzfeldt-Jakob disease, and type II diabetes.
Identifying novel amyloid protein precursors is crucial for understanding disease pathology.

Purpose of the Study:

To develop a computational tool for identifying novel protein precursors of amyloid fibril deposits.
To enhance the understanding of the pathological processes underlying amyloidoses.

Main Methods:

Development of a novel classifier named ENTAIL.
Utilized over 4000 molecular descriptors for feature extraction.
Employed a Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type.

Main Results:

The ENTAIL classifier demonstrated high performance on a balanced dataset.
Achieved an accuracy of 81.80% on the test set.
Reported a sensitivity (SN) of 100%, specificity (SP) of 63.63%, and Matthews Correlation Coefficient (MCC) of 0.683.

Conclusions:

The developed ENTAIL classifier shows significant potential in identifying amyloid precursors.
Performance metrics indicate robust predictive capabilities, particularly on balanced datasets.
This advancement contributes to a deeper understanding of amyloidosis pathogenesis.