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作者:

Wu, Jiahui (Wu, Jiahui.) | Liu, Bo (Liu, Bo.) | Zhang, Jidong (Zhang, Jidong.) | Wang, Zhihan (Wang, Zhihan.) | Li, Jianqiang (Li, Jianqiang.)

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摘要:

PurposeSequenced 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.ResultsIn 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.

关键词:

Feature extraction Deep learning Protein-protein interaction Graph neural network

作者机构:

  • [ 1 ] [Wu, Jiahui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Jidong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Zhihan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Jianqiang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Liu, Bo]Massey Univ, Sch Math & Computat Sci, Auckland 0745, New Zealand

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来源 :

BMC BIOINFORMATICS

ISSN: 1471-2105

年份: 2023

期: 1

卷: 24

3 . 0 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 6

SCOPUS被引频次: 8

ESI高被引论文在榜: 0 展开所有

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