Genomics Proteomics Bioinformatics
December 2016
Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics.
View Article and Find Full Text PDFObjective: The study was carried out to examine the association between apolipoprotein B (ApoB) EcoRI polymorphism (E vs. E) (rs1042031) and coronary heart disease (CHD) risk by systematically analyzing multiple independent studies.
Methods: The Hardy-Weinberg equilibrium (HWE) test was applied to assess genotype frequency distribution in healthy controls.
Objective: To examine the association between apolipoprotein B (ApoB) XbaI polymorphisms (rs693) and coronary heart disease (CHD) risk among the Han Chinese population by systematically analyzing multiple independent studies.
Methods: The Hardy-Weinberg equilibrium test was applied to check genetic equilibrium among genotypes for the selected literatures. The quality of the studies was assessed by using the NewcastleOttawa Scale.
Biological pathways have been widely used in gene function studies; however, the current knowledge for biological pathways is per se incomplete and has to be further expanded. Bioinformatics prediction provides us a cheap but effective way for pathway expansion. Here, we proposed a novel method for biological pathway prediction, by intergrating prior knowledge of protein?protein interactions and Gene Ontology (GO) database.
View Article and Find Full Text PDFGenomics Proteomics Bioinformatics
February 2014
Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers.
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