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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Using Gene Ontology to Annotate and Prioritize Microarray Data.

Marianna Milano¹

¹Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, Italy. m.milano@unicz.it.

Methods in Molecular Biology (Clifton, N.J.)

|December 13, 2021

Summary

This summary is machine-generated.

Omics science faces challenges in filtering disease-associated genes from high-throughput data. Gene prioritization methods use ontologies to identify key molecules linked to specific diseases for clinical relevance.

Keywords:

Data Annotation Gene Ontology Gene Prioritization Microarray NGS

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput experiments generate vast amounts of candidate disease-associated genes and proteins.
Extracting meaningful knowledge and filtering promising candidates is a significant challenge in omics science.
Identifying specific molecules crucial for particular diseases is a key focus in clinical research.

Purpose of the Study:

To introduce and discuss computational approaches for gene prioritization.
To highlight the role of domain-specific knowledge, particularly ontologies, in gene identification.
To address the challenge of filtering relevant genes from large omics datasets for clinical applications.

Main Methods:

Utilizing computational approaches, specifically gene prioritization methods.
Leveraging domain-specific knowledge encoded in ontologies.
Applying these methods to filter candidate genes from high-throughput experimental results.

Main Results:

Gene prioritization methods effectively identify genes most relevant to a specific disease.
The use of ontologies enhances the accuracy and specificity of gene identification.
These computational strategies aid in filtering large datasets to pinpoint key molecular players.

Conclusions:

Computational gene prioritization is essential for knowledge extraction in omics science.
Ontology-driven approaches improve the identification of disease-related genes.
This facilitates the focus on critical molecules for specific diseases in clinical settings.