TOP-BIOCom: A Feature Fusion-based Prediction of Protein Complexes from PPI Networks

These findings suggest that integrating topological and biological features can provide a holistic view of protein complexes enhancing prediction accuracy and aiding in drug discovery and understanding cellular mechanisms.

Authors

Institutions

Publication Details

Journal
Current Computer - Aided Drug Design
Published
2025-10-31
DOI
https://doi.org/10.2174/0115734099408938251017040557
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TOP-BIOCom: A Feature Fusion-based Prediction of Protein Complexes from PPI Networks

Muhammad Hasan Jamal, Muhammad Waqas Anwar, Matloob Hussain
Current Computer - Aided Drug Design
Bioinformatics and Genomic Networks
article

TOP-BIOCom: A Feature Fusion-based Prediction of Protein Complexes from PPI Networks

Muhammad Hasan Jamal, Muhammad Waqas Anwar, Matloob Hussain
article en

Abstract

These findings suggest that integrating topological and biological features can provide a holistic view of protein complexes enhancing prediction accuracy and aiding in drug discovery and understanding cellular mechanisms.

Current Computer - Aided Drug DesignVol. 22
COMSATS University Islamabad (PK), Government College University, Lahore (PK)
Openalex Percentile: Top 71%
Bioinformatics and Genomic Networks
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.