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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Graph2GO: a multi-modal attributed network embedding method for inferring protein functions.

Kunjie Fan¹, Yuanfang Guan², Yan Zhang^1,3

¹Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.

|August 10, 2020

Summary

This summary is machine-generated.

Graph2GO, a novel graph-based model, accurately predicts protein functions by integrating diverse biological data. This computational approach accelerates biological research by providing testable hypotheses for large-scale experiments.

Keywords:

attributed network embedding graph neural network multi-modal model protein function prediction representation learning

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Experimental protein function characterization is time-consuming and expensive.
Accurate computational methods are crucial for predicting protein functions and guiding experiments.
Gene Ontology (GO) is a key resource for annotating protein functions.

Purpose of the Study:

To develop an advanced computational model for predicting protein functions.
To integrate heterogeneous biological data for improved prediction accuracy.
To provide a user-friendly tool for protein function analysis.

Main Methods:

Developed Graph2GO, a multi-modal graph-based representation learning model.
Integrated sequence similarity networks, protein-protein interaction networks, and protein features (sequence, location, domains).
Utilized attributed network representation learning for modeling interactions and features.

Main Results:

Graph2GO achieved state-of-the-art performance in protein function prediction.
Outperformed baseline (BLAST) and existing methods (Mashup, deepNF).
Demonstrated robustness across multiple species and provided a web server for on-the-fly analysis.

Conclusions:

Graph2GO is the first model to use attributed network representation learning for protein function prediction.
The model's performance is promising and can be extended with more features.
Graph2GO is applicable to other biological network analyses and downstream machine learning tasks.