MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models

Compared to the state-of-the-art methods, MESM achieved improvements of 8.77%, 4.98%, 7.48%, and 6.08% on SHS27k, SHS148k, SYS30k, and SYS60k, respectively. The experimental results demonstrate that MESM exhibits significant improvements in PPI prediction performance.

Authors

Feng Wang (ORCID: https://orcid.org/0000-0002-0275-8267)
Shan Chang (ORCID: https://orcid.org/0000-0002-2911-5624)
J.Y. Chu (ORCID: https://orcid.org/0009-0007-4841-165X)
Liyan Shen

Institutions

Suzhou Vocational University (CN)
Jiangsu University of Technology (CN)
Changzhou University (CN)
Taizhou University (CN)

Publication Details

Journal: BMC Biology
Published: 2025-08-10
DOI: https://doi.org/10.1186/s12915-025-02356-y
Citations: 1
Primary Topic: Bioinformatics and Genomic Networks
Type: article
Field-Weighted Citation Impact: 0.81

Funders

National Natural Science Foundation of China

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models

Feng Wang, Shan Chang, J.Y. Chu, Liyan Shen

Aug 10, 2025

1 citations

BMC Biology

Bioinformatics and Genomic Networks

0.81

article

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models

Feng Wang, Shan Chang, J.Y. Chu, Liyan Shen

August 10, 2025article en

1 citations

Abstract

Compared to the state-of-the-art methods, MESM achieved improvements of 8.77%, 4.98%, 7.48%, and 6.08% on SHS27k, SHS148k, SYS30k, and SYS60k, respectively. The experimental results demonstrate that MESM exhibits significant improvements in PPI prediction performance.

BMC BiologyVol. 23(1)

Suzhou Vocational University (CN), Jiangsu University of Technology (CN), Changzhou University (CN), Taizhou University (CN)

National Natural Science Foundation of China

Openalex Percentile: Top 25%

Bioinformatics and Genomic Networks

0.81

DOI Open Access PDF OpenAlex

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.