SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.
Int J Mol Sci
; 25(10)2024 May 16.
Article
em En
| MEDLINE
| ID: mdl-38791479
ABSTRACT
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.
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Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Biologia Computacional
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article