Personalized diagnosis by cached solutions with hypertension as a study model
Genet. mol. res. (Online)
; 5(4): 856-867, 2006. tab, ilus, graf
Article
em En
| LILACS
| ID: lil-482072
Biblioteca responsável:
BR1.1
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 [quot ]disease stage[quot ] 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 [quot ]risk[quot ] 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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
LILACS
Assunto principal:
Polimorfismo Genético
/
Sistema Renina-Angiotensina
/
Reconhecimento Automatizado de Padrão
/
Diagnóstico por Computador
/
Predisposição Genética para Doença
/
Hipertensão
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Genet. mol. res. (Online)
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Brasil