Clinical bioinformatics is currently growing and is based on integrating clinical and omics data aiming to develop personalized medicine. Thus the introduction of novel technologies to investigate the relationship between clinical states and biological machinery may help the development of this field.
High-throughput experimental platforms such as single nucleotide polymorphism (SNP) microarray can study the relationship between the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow, for instance, to find genetic variants in patients who present different drug responses in pharmacogenomics and clinical studies. Statistical and data mining software tools can help researchers determine the association between SNPs and patients’ clinical conditions responsible for the specific drug response.
This is only a partial result because the statistical and data mining analysis of microarray data provides a list of SNPs referring to specific genes that are still detached from the affected biological machinery functions.
Pathway enrichment analysis (PEA) methods seek to overcome the problem of interpreting overwhelmingly large lists of essential genes detached from biological context, which are the main output of most basic high-throughput data analysis, such as SNP microarray analysis.
Thus, PEA enables the researcher to generate a new hypothesis, design subsequent experiments, and further validate their findings, for instance, by identifying the biological roles of candidate genes in designing new cancer therapies.