Challenges exist for each stage of an integration process, including the creation of an empirical gene list across species and platforms, scoring the information, and then evaluating the scoring system itself. For example, once various data are collected, identifying the best way to integrate them poses a problem since the criteria for selecting gene lists often differ substantially across studies. Specifically in microarray studies, the expression of gene-specific transcripts is selected via statistical threshold(s), but individual genes can have multiple transcripts that may differ in their abundance [8]. Therefore, a given gene can yield multiple expression values through microarray or next generation RNA sequencing (RNA-Seq) analyses. Likewise, human genetic association studies test multiple genetic markers, usually single nucleotide polymorphisms (SNPs), across a gene. In contrast, the results of genetic linkage or quantitative trait locus (QTL) studies in humans or mice can span tens of megabases and contain potentially hundreds of genes. Furthermore, low replication rates and identification of non-functional markers in most studies makes the search for true genetic signals difficult [9-11]. While there are issues with data reduction or
