In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the...

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Bibliographic Details
Main Authors: Marco Anteghini, Vitor Martins dos Santos, Edoardo Saccenti
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:International Journal of Molecular Sciences
Subjects:
protein sequence encoding and embedding
machine learning
neural networks
subcellular localisation
sub-peroxisomal localisation
sub-mitochondrial localisation
Online Access:https://www.mdpi.com/1422-0067/22/12/6409

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