SCLpred‐MEM : Subcellular localization prediction of membrane proteins by deep N‐to‐1 convolutional neural networks
| Autor: | Catherine Mooney, Manaz Kaleel, Liam Ellinger, Clodagh Lalor, Gianluca Pollastri |
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| Rok vydání: | 2021 |
| Předmět: |
Matching (graph theory)
Computer science Computational biology Biochemistry Convolutional neural network 03 medical and health sciences Deep Learning Structural Biology Redundancy (engineering) Animals Humans Databases Protein Molecular Biology 030304 developmental biology 0303 health sciences Membranes business.industry Deep learning 030302 biochemistry & molecular biology Fungi Computational Biology Membrane Proteins Plants Matthews correlation coefficient Subcellular localization Membrane protein Test set Neural Networks Computer Artificial intelligence business |
| Zdroj: | Proteins: Structure, Function, and Bioinformatics. 89:1233-1239 |
| ISSN: | 1097-0134 0887-3585 |
| DOI: | 10.1002/prot.26144 |
| Popis: | The knowledge of the subcellular location of a protein is a valuable source of information in genomics, drug design, and various other theoretical and analytical perspectives of bioinformatics. Due to the expensive and time-consuming nature of experimental methods of protein subcellular location determination, various computational methods have been developed for subcellular localization prediction. We introduce "SCLpred-MEM," an ab initio protein subcellular localization predictor, powered by an ensemble of Deep N-to-1 Convolutional Neural Networks (N1-NN) trained and tested on strict redundancy reduced datasets. SCLpred-MEM is available as a web-server predicting query proteins into two classes, membrane and non-membrane proteins. SCLpred-MEM achieves a Matthews correlation coefficient of 0.52 on a strictly homology-reduced independent test set and 0.62 on a less strict homology reduced independent test set, surpassing or matching other state-of-the-art subcellular localization predictors. |
| Databáze: | OpenAIRE |
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