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

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A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.

Pratik Dutta¹, Sriparna Saha², Sanket Pai³

¹Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, 801103, India. pratik.pcs16@iitp.ac.in.

Scientific Reports

|January 22, 2020

Summary

This summary is machine-generated.

This study introduces a novel protein-protein interaction-based generative model for efficient gene clustering in computational bioinformatics. The model leverages weak supervision, outperforming existing methods in accurately partitioning genes for disease gene identification.

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Area of Science:

Computational bioinformatics
Systems biology
Genomics

Background:

Identifying disease-related genes is crucial for medical diagnosis and understanding cellular mechanisms.
Accurate gene clustering is essential for elucidating gene functions, particularly for disease-associated genes.
Ensemble clustering methods offer a promising approach to integrate diverse clustering results for improved gene partitioning.

Purpose of the Study:

To develop a protein-protein interaction-based generative model for efficient gene clustering.
To enhance gene clustering accuracy by utilizing protein interaction data as latent variables.
To explore the use of weak supervision sources, avoiding reliance on ground truth information.

Main Methods:

Developed a generative model incorporating protein-protein interaction data.
Employed weak supervision by integrating a multi-objective optimization clustering technique and the Gene Ontology Consortium (GOC) knowledge base.
Utilized the generative model to assign probabilistic labels to genes based on weak supervision.

Main Results:

The proposed generative model demonstrated enhanced efficiency in inferring probabilistic gene labels.
Comparative analysis using silhouette score, Biological Homogeneity Index (BHI), and Biological Stability Index (BSI) showed superior performance over state-of-the-art techniques.
The model effectively performs gene clustering using weak supervision and protein interaction information.

Conclusions:

The protein-protein interaction-based generative model provides an effective and accurate approach for gene clustering.
Weak supervision, combined with protein interaction data, significantly improves gene partitioning for biological applications.
This method offers a robust alternative for disease gene identification and functional genomics studies.