Severe Dengue Prognosis Using Human Genome Data and Machine Learning Academic Article uri icon

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.

altmetric score

  • 2

author list (cited authors)

  • Davi, C., Pastor, A., Oliveira, T., de Lima Neto, F. B., Braga-Neto, U., Bigham, A. W., ... Acioli-Santos, B.

citation count

  • 18

publication date

  • October 2019