One of the challenges with supervised interpretation of -omics data analyses is the limitation of existing knowledge databases. An association is typically established by an “enrichment” score of a gene to a member of a known pathway. While this technique is one of the most prevalent among researchers in understanding findings from gene expression studies, it also prevents any significant findings of new associations.
To highlight the uses and benefits of an unsupervised approach, we present a case study where a public data set was analyzed using clustering of differentially expressed genes in Alzheimer patients.
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