Evidence map›Paper›PMID 40728607›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2025

Graph Neural Network-Based Approaches for Protein Function Prediction.

Meenal Chaudhari, Soufia Bahmani, Pawel Pratyush, Steven Garrett, Neel J Thapa, Dukka B Kc

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Meenal ChaudhariCollege of Applied Sciences and Technology, Illinois State University, Normal, IL, USA.
Soufia BahmaniCollege of Computing, Michigan Technological University, Houghton, MI, USA.
Pawel PratyushGolisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA.
Steven GarrettCollege of Computing, Michigan Technological University, Houghton, MI, USA.
Neel J ThapaNorthwest High School, Germantown, MD, USA.
Dukka B KcGolisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA. dkcvcs@rit.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein functions often involve a dynamic interplay that covers a variety of molecular interactions that can be represented and analyzed in a 3-dimensional space. To this end, researchers have applied graph neural networks (GNNs) that effectively model such spaces as a promising methodology to predict protein functions. We discuss the graph-based representations of proteins that are applied to different prediction tasks, which include graphs at various levels of granularity: atomic, residue, and multi-scale. We also review various protein function prediction tools that rely on GNN architectures that learn representations from protein graphs, specifically in the context of the Gene Ontology prediction and protein-protein interaction prediction. GNN-based methods leverage the underlying structural knowledge and offer a promising future in improving the quality of the protein function predictions.

Indexed as

Computational BiologyNeural Networks, ComputerProteinsAlgorithmsDatabases, ProteinGene OntologyGraph Neural NetworksHumansProtein Interaction MappingSoftwareProteinsGene ontology termGeometric deep learningGraph attention networksGraph neural networkProtein functionProtein–protein interactions

Identifiers

PMID40728607

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.