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A High Efficient Biological Language Model for Predicting Protein–Protein Interactions
Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are const...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406841/ https://www.ncbi.nlm.nih.gov/pubmed/30717470 http://dx.doi.org/10.3390/cells8020122 |
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author | Wang, Yanbin You, Zhu-Hong Yang, Shan Li, Xiao Jiang, Tong-Hai Zhou, Xi |
author_facet | Wang, Yanbin You, Zhu-Hong Yang, Shan Li, Xiao Jiang, Tong-Hai Zhou, Xi |
author_sort | Wang, Yanbin |
collection | PubMed |
description | Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems. |
format | Online Article Text |
id | pubmed-6406841 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64068412019-03-19 A High Efficient Biological Language Model for Predicting Protein–Protein Interactions Wang, Yanbin You, Zhu-Hong Yang, Shan Li, Xiao Jiang, Tong-Hai Zhou, Xi Cells Article Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems. MDPI 2019-02-03 /pmc/articles/PMC6406841/ /pubmed/30717470 http://dx.doi.org/10.3390/cells8020122 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yanbin You, Zhu-Hong Yang, Shan Li, Xiao Jiang, Tong-Hai Zhou, Xi A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title_full | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title_fullStr | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title_full_unstemmed | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title_short | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions |
title_sort | high efficient biological language model for predicting protein–protein interactions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406841/ https://www.ncbi.nlm.nih.gov/pubmed/30717470 http://dx.doi.org/10.3390/cells8020122 |
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