The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction.
Biological function analysis based on MAQC-II breast cancer dataset finds that our applied feature selection methods including RFA, GLGS, LOOCSFS, and SVMRFE can generate features containing a significant portion of known cancer related genes for both pCR and erpos endpoints (Tables 1 10).
The functional analysis comprised the following two independent approaches: a biological pathway analysis based on the publicly available Kyoto encyclopedia of genes and genomes (KEGG) pathways and a network-based analysis that was performed using the ingenuity pathway analysis (IPA) software.
In this study a genome wide association scan (GWAS) in the DH and DJ was performed for a detailed milk fatty acid (FA) profile using the HD bovine SNP array and subsequently a biological pathway analysis based on the SNP data was performed.
This provides an unbiased biological gene enrichment analysis based on biological properties (GO-terms) assigned per gene.
GO enrichment analysis based on biological process showed that the 243 predicted target genes of flounder were clustered into 296 GO terms.
We chose covariates for this analysis based on biological plausibility as confounders (i.e., factors likely to be associated with both HFE genotype and blood lead levels).
The gene ontology analysis based on biological process and enrichment analysis was performed by the right-side hyper-geometric statistic test and its probability value was corrected by Bonferroni's method [ 52].
To elucidate pathways with more biological relevance, we further conducted pathway analysis based on a subset of SNPs with known regulatory functions.
Of these genes, seven candidates were selected for further analysis based on proposed biological function and fold induction repression by rhTRAIL (Table 1B).
