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Sequence-based prediction of protein protein interaction using a deep-learning algorithm

BMC bioinformatics, 2017-05, Vol.18 (1), p.277-277, Article 277 [Peer Reviewed Journal]

Copyright BioMed Central 2017 ;The Author(s). 2017 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-017-1700-2 ;PMID: 28545462

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Title:
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Author: Sun, Tanlin ; Zhou, Bo ; Lai, Luhua ; Pei, Jianfeng
Subjects: Accuracy ; Algorithms ; Amino Acid Sequence ; Amino acids ; Animals ; Aversion learning ; Bioinformatics ; Biological activity ; Caenorhabditis elegans - metabolism ; Computer applications ; Datasets ; Deep learning ; Deoxyribonucleic acid ; DNA ; Drosophila - metabolism ; Escherichia coli - metabolism ; Genomes ; High-Throughput Screening Assays ; Humans ; Internet ; Learning algorithms ; Mathematical models ; Neural networks ; Predictions ; Principal components analysis ; Protein interaction ; Protein Interaction Mapping - methods ; Protein-protein interaction ; Proteins ; Proteins - chemistry ; Proteins - metabolism ; Robustness (mathematics) ; Serial learning ; Signal transduction ; Teaching methods ; Therapy ; User-Computer Interface
Is Part Of: BMC bioinformatics, 2017-05, Vol.18 (1), p.277-277, Article 277
Description: Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.
Publisher: England: BioMed Central
Language: English
Identifier: ISSN: 1471-2105
EISSN: 1471-2105
DOI: 10.1186/s12859-017-1700-2
PMID: 28545462
Source: Geneva Foundation Free Medical Journals at publisher websites
Springer Open Access
MEDLINE
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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Sequence-based prediction of protein protein interaction using a deep-learning algorithm

Copyright BioMed Central 2017 ;The Author(s). 2017 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-017-1700-2 ;PMID: 28545462

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