Severe Dengue Prognosis Using Human Genome Data and Machine Learning.
IEEE Trans Biomed Eng
; 66(10): 2861-2868, 2019 10.
Article
en En
| MEDLINE
| ID: mdl-30716030
ABSTRACT
Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate. OBJECTIVE:
We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data.METHODS:
One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity single nucleotide polymorphisms (SNPs). Our model uses a support vector machine algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue.RESULTS:
The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively.CONCLUSION:
The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions.SIGNIFICANCE:
Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian based and genetically influenced diseases.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Genoma Humano
/
Dengue Grave
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Humans
País/Región como asunto:
America do sul
/
Brasil
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Año:
2019
Tipo del documento:
Article