Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 66, Issue 10, Pages 2861-2868Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2019.2897285
Keywords
Dengue genetics; severe dengue; complex genome signatures; machine learning
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Funding
- PROEP-FIOCRUZ-CPqAM [APQ-1597-2-02/15CNPq]
- PAPES VII [401910/2015-6]
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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 un-infected 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.
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