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Updated: May 1, 2026

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

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

Published on: October 13, 2023

Approaches for recognizing disease genes based on network.

Quan Zou¹, Jinjin Li¹, Chunyu Wang²

¹School of Information Science and Technology, Xiamen University, Xiamen 361005, China.

Biomed Research International

|April 8, 2014

Summary

This summary is machine-generated.

Identifying disease genes is crucial for healthcare and understanding biological pathways. This study reviews network-based machine learning methods for recognizing disease genes, highlighting challenges and future directions.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genetic abnormalities are linked to diseases, making disease gene recognition a key biological goal.
Understanding gene functions, pathways, and interactions can improve healthcare.
Limited large-scale datasets for gene-gene, disease-disease, and gene-disease associations pose a challenge.

Purpose of the Study:

To review the relationship between diseases and genes.
To summarize network-based approaches for disease gene recognition.
To analyze challenges and future research directions in the field.

Main Methods:

Review of existing literature on disease gene recognition using network-based methods.
Analysis of machine learning approaches applied to biological networks.
Identification of core problems and challenges in current methodologies.

Main Results:

Gene-disease relationships are fundamental to understanding disease etiology.
Network-based machine learning offers promising avenues for disease gene identification.
Current methods face challenges related to data availability and methodological limitations.

Conclusions:

Further development of large-scale association datasets is essential.
Advanced network analysis and machine learning techniques are needed.
Future research should focus on overcoming current challenges for improved disease gene discovery.