DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning

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dc.citation.issue1
dc.citation.volume24
dc.contributor.authorWu J
dc.contributor.authorLiu B
dc.contributor.authorZhang J
dc.contributor.authorWang Z
dc.contributor.authorLi J
dc.coverage.spatialEngland
dc.date.accessioned2024-06-26T01:05:44Z
dc.date.available2024-06-26T01:05:44Z
dc.date.issued2023-12
dc.description.abstractPURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
dc.description.confidentialfalse
dc.edition.editionDecember 2023
dc.format.pagination473-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/38097937
dc.identifier.citationWu J, Liu B, Zhang J, Wang Z, Li J. (2023). DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.. BMC Bioinformatics. 24. 1. (pp. 473-).
dc.identifier.doi10.1186/s12859-023-05594-5
dc.identifier.eissn1471-2105
dc.identifier.elements-typejournal-article
dc.identifier.issn1471-2105
dc.identifier.number473
dc.identifier.pii10.1186/s12859-023-05594-5
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70007
dc.languageeng
dc.publisherBioMed Central Ltd
dc.publisher.urihttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05594-5
dc.relation.isPartOfBMC Bioinformatics
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectDeep learning
dc.subjectFeature extraction
dc.subjectGraph neural network
dc.subjectProtein–protein interaction
dc.subjectProtein Interaction Mapping
dc.subjectDeep Learning
dc.subjectNeural Networks, Computer
dc.subjectAmino Acid Sequence
dc.subjectProteins
dc.titleDL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning
dc.typeJournal article
pubs.elements-id485272
pubs.organisational-groupOther
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