Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes.

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

Agapito, Giuseppe;Milano, Marianna;Cannataro, Mario
2022-01-01

Abstract

Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes.
2022
Biological pathways
Data mining network
Differential expressed genes
Network analysis
Pathway enrichment analysis
SNPs
Statistical analysis
Gene Expression
Oligonucleotide Array Sequence Analysis
Polymorphism, Single Nucleotide
Biological Phenomena
Gene Expression Profiling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/79999
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