An integrative model for in-silico clinical-genomics discovery science.
Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.
Lussier
Yves A
YA
Department of Medical Informatics, College of Physicians and Surgeons, Columbia University, New York, NY, USA. Lussier@dmi.columbia.edu
Sarkar
Indra Nell
IN
Cantor
Michael
M
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D005240:Feasibility Studies; D015870:Gene Expression; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D016247:Information Storage and Retrieval; D010506:Periodicals as Topic; D039061:Systematized Nomenclature of Medicine