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

Autor: Luhua Lai, Jianfeng Pei, Tanlin Sun, Bo Zhou
Rok vydání: 2017
Předmět:
0301 basic medicine
Computer science
lcsh:Computer applications to medicine. Medical informatics
Machine learning
computer.software_genre
Biochemistry
Field (computer science)
Protein–protein interaction
User-Computer Interface
03 medical and health sciences
Protein-protein interaction
0302 clinical medicine
Structural Biology
Robustness (computer science)
Protein Interaction Mapping
Escherichia coli
Animals
Humans
Amino Acid Sequence
Caenorhabditis elegans
lcsh:QH301-705.5
Molecular Biology
Internet
Sequence
Protein function
business.industry
Applied Mathematics
Deep learning
Proteins
Autoencoder
High-Throughput Screening Assays
Computer Science Applications
030104 developmental biology
lcsh:Biology (General)
030220 oncology & carcinogenesis
lcsh:R858-859.7
Drosophila
Artificial intelligence
DNA microarray
business
computer
Algorithm
Algorithms
Research Article
Zdroj: BMC Bioinformatics, Vol 18, Iss 1, Pp 1-8 (2017)
BMC Bioinformatics
ISSN: 1471-2105
DOI: 10.1186/s12859-017-1700-2
Popis: Background 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1700-2) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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