Accuracy of a machine learning method based on structural and locational information from AlphaFold2 for predicting the pathogenicity of TARDBP and FUS gene variants in ALS

Accuracy of a machine learning method based on structural and locational information from AlphaFold2 for predicting the pathogenicity of TARDBP and FUS gene variants in ALS

Abstract Background In the sporadic form of amyotrophic lateral sclerosis (ALS), the pathogenicity of rare variants in the causative genes characterizing the familial form remains largely unknown. To predict the pathogenicity of such variants, in silico analysis is commonly used. In some ALS causati...

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Bibliographic Details
Main Authors: Yuya Hatano, Tomohiko Ishihara, Osamu Onodera
Format: Article
Language:English
Published: BMC 2023-05-01
Series:BMC Bioinformatics
Subjects:
MOVA
Missense variant
Prediction tool
Amyotrophic lateral sclerosis
AlphaFold2
Online Access:https://doi.org/10.1186/s12859-023-05338-5

Internet

https://doi.org/10.1186/s12859-023-05338-5

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