title: Personalized diagnosis by cached solutions with hypertension as a study model.
abstract: Statistical modeling of links between genetic profiles with environmental and clinical data to aid in medical diagnosis is a challenge. Here, we present a computational approach for rapidly selecting important clinical data to assist in medical decisions based on personalized genetic profiles. What could take hours or days of computing is available on-the-fly, making this strategy feasible to implement as a routine without demanding great computing power. The key to rapidly obtaining an optimal/nearly optimal mathematical function that can evaluate the "disease stage" by combining information of genetic profiles with personal clinical data is done by querying a precomputed solution database. The database is previously generated by a new hybrid feature selection method that makes use of support vector machines, recursive feature elimination and random sub-space search. Here, to evaluate the method, data from polymorphisms in the renin-angiotensin-aldosterone system genes together with clinical data were obtained from patients with hypertension and control subjects. The disease "risk" was determined by classifying the patients' data with a support vector machine model based on the optimized feature; then measuring the Euclidean distance to the hyperplane decision function. Our results showed the association of renin-angiotensin-aldosterone system gene haplotypes with hypertension. The association of polymorphism patterns with different ethnic groups was also tracked by the feature selection process. A demonstration of this method is also available online on the project's web site.
authors.affiliation: Programa de Engenharia de Sistemas e Computação, COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brasil. carvalhopc@cos.ufrj.br
authors.forename: P C
authors.initials: PC
authors.lastname: Carvalho
authors.forename: S S
authors.initials: SS
authors.lastname: Freitas
authors.forename: A B
authors.initials: AB
authors.lastname: Lima
authors.forename: M
authors.initials: M
authors.lastname: Barros
authors.forename: I
authors.initials: I
authors.lastname: Bittencourt
authors.forename: W
authors.initials: W
authors.lastname: Degrave
authors.forename: I
authors.initials: I
authors.lastname: Cordovil
authors.forename: R
authors.initials: R
authors.lastname: Fonseca
authors.forename: M G C
authors.initials: MG
authors.lastname: Carvalho
authors.forename: R S
authors.initials: RS
authors.lastname: Moura Neto
authors.forename: P H
authors.initials: PH
authors.lastname: Cabello
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000175:diagnosis* / Q000235:genetics; D008297:Male; D008957:Models, Genetic; D010363:Pattern Recognition, Automated; D011110:Polymorphism, Genetic / Q000235:genetics*; D012084:Renin-Angiotensin System / Q000235:genetics*; D015203:Reproducibility of Results