Clinical-Genomic Analysis for Disease Prediction

Recent advances in genomic research have generated vast amounts of information that can help identify individuals who differ in their susceptibility to a particular disease or response to a specific treatment. This information may offer solutions for the treatment of complex chronic diseases that are influenced by a wide array of factors. This vast amount of information brings critical challenges in applying advanced technology to synthesize clinical-genomic patient data. Synthesizing this information is necessary to derive the knowledge that would empower physicians to provide personalized care with the best possible therapeutic interventions.

We used statistical methods and data mining approaches to understand clinical-genomic risk factors that differentiate Type II Diabetes cases from healthy controls. We investigated whether inclusion of genomic risk factors in conjunction with clinical information improves classification accuracy. We also demonstrate how a biased and an unbiased method for selection of risk associated single nucleotide polymorphisms (SNPs) effect clustering along with clinical information. We determined the optimal method based on its clustering performance.

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classification, clinical-genomic risk, clustering, snps


University of Maryland, Baltimore County

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