dc.creator | Carvalho, P. C. | |
dc.creator | Freitas, S. S. | |
dc.creator | Lima, A; B. | |
dc.creator | Barros, M. | |
dc.creator | Bittencourt, I. | |
dc.creator | Degrave, Win | |
dc.creator | Cordovil, I. | |
dc.creator | Fonseca, R. | |
dc.creator | Carvalho, M. G. C. | |
dc.creator | Moura Neto, R. S. | |
dc.creator | Cabello, P. H. | |
dc.date | 2020-01-02T18:38:16Z | |
dc.date | 2020-01-02T18:38:16Z | |
dc.date | 2006 | |
dc.date.accessioned | 2023-09-26T21:03:17Z | |
dc.date.available | 2023-09-26T21:03:17Z | |
dc.identifier | CARVALHO, P. C. et al. Personalized diagnosis by cached solutions with hypertension as a study model. Genetics and Molecular Research, v. 5, n. 4, p. 856-867, 2006. | |
dc.identifier | 1676-5680 | |
dc.identifier | https://www.arca.fiocruz.br/handle/icict/38947 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8868399 | |
dc.description | 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 reninangiotensin-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. | |
dc.format | application/pdf | |
dc.language | eng | |
dc.publisher | FUNPEC-RP | |
dc.rights | open access | |
dc.subject | Polimorfismos genéticos | |
dc.subject | Hipertensão essencial | |
dc.subject | Riscos ambientais | |
dc.subject | Máquinas de vetores de suporte | |
dc.subject | Seleção de recursos | |
dc.subject | Genetic polymorphisms | |
dc.subject | Essential hypertension | |
dc.subject | Environmental risks | |
dc.subject | Support vector machines | |
dc.subject | Feature selection | |
dc.title | Personalized diagnosis by cached solutions with hypertension as a study model | |
dc.type | Article | |