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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Pathway-level disease data mining through hyper-box principles.

Lingjian Yang¹, Chrysanthi Ainali², Aristotelis Kittas³

¹Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK.

Mathematical Biosciences

|September 23, 2014

Summary

This summary is machine-generated.

This study introduces a pathway-based approach for disease classification using gene expression data. It highlights the potential of functional gene sets and hyper-box models to link gene patterns to disease phenotypes.

Keywords:

Disease classification Hyper-box-representation Mathematical programming Mixed integer optimisation Pathway-based classification

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Traditional single-gene analysis in microarray data is limited.
Pathway-based analysis offers deeper insights into gene expression and disease.

Purpose of the Study:

To develop robust pipelines for relating genotypic functional modules to disease phenotypes.
To present a pathway-based approach for disease classification using mathematical programming.

Main Methods:

Discussed methodologies employing gene groups for disease classification.
Presented a hyper-box mathematical programming model for pathway-based classification.
Extracted and discussed association rules for pathway-specific molecular patterns.

Main Results:

Demonstrated the potential of gene sets from disease-relevant pathways for uncovering expression-to-phenotype relations.
Illustrated hyper-box classification's utility in assessing pathway predictive power.
Showcased the identification of specific gene effects in disease phenotype prediction.

Conclusions:

Pathway-based gene set analysis is a promising strategy for disease classification.
Hyper-box classification effectively assesses functional pathway predictive power and gene effects.