Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information

Overall, the proposed HI-PPI effectively addresses the limitations of existing PPI prediction methods. By leveraging the hierarchical structure of PPI network, HI-PPI significantly enhances the accuracy and robustness of PPI predictions.

Authors

Institutions

Publication Details

Journal
BMC Biology
Published
2025-08-03
DOI
https://doi.org/10.1186/s12915-025-02359-9
Citations
2
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
1.61

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information

Weizhuo Li, Jing Jiang, Xiaofeng Cao, Tao Tang et al.
2 citations
BMC Biology
Protein Structure and Dynamics
1.61
article

Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information

Weizhuo Li, Jing Jiang, Xiaofeng Cao, Tao Tang, Sisi Yuan, Peng Wang, Yuansheng Liu, Taiguang Shen
article en
2 citations

Abstract

Overall, the proposed HI-PPI effectively addresses the limitations of existing PPI prediction methods. By leveraging the hierarchical structure of PPI network, HI-PPI significantly enhances the accuracy and robustness of PPI predictions.

BMC BiologyVol. 23(1)
Tongji University (CN), Hunan University (CN), Nanjing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 16%
Protein Structure and Dynamics
1.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.