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Severe Dengue Prognosis Using Human Genome Data and Machine Learning

Davi, Caio ; Pastor, Andre ; Oliveira, Thiego ; Neto, Fernando B. de Lima ; Braga-Neto, Ulisses ; Bigham, Abigail W. ; Bamshad, Michael ; Marques, Ernesto T. A. ; Acioli-Santos, Bartolomeu

IEEE transactions on biomedical engineering, 2019-10, Vol.66 (10), p.2861-2868 [Periódico revisado por pares]

United States: IEEE

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Título:
Severe Dengue Prognosis Using Human Genome Data and Machine Learning
Autor: Davi, Caio ; Pastor, Andre ; Oliveira, Thiego ; Neto, Fernando B. de Lima ; Braga-Neto, Ulisses ; Bigham, Abigail W. ; Bamshad, Michael ; Marques, Ernesto T. A. ; Acioli-Santos, Bartolomeu
Assuntos: Algorithms ; Artificial intelligence ; Artificial neural networks ; Bioinformatics ; Classification ; complex genome signatures ; Dengue fever ; Dengue genetics ; Dengue hemorrhagic fever ; Disease control ; Diseases ; DNA ; Feature extraction ; Fever ; Genomes ; Genomics ; Innate immunity ; Learning algorithms ; Learning theory ; Machine learning ; Model accuracy ; Neural networks ; Nucleotides ; Phenotypes ; severe dengue ; Single-nucleotide polymorphism ; Support vector machines ; Vector-borne diseases ; Viral diseases ; Viruses (medical)
É parte de: IEEE transactions on biomedical engineering, 2019-10, Vol.66 (10), p.2861-2868
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SourceType-Scholarly Journals-1
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Descrição: 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.
Editor: United States: IEEE
Idioma: Inglês

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Severe Dengue Prognosis Using Human Genome Data and Machine Learning

Davi, Caio ; Pastor, Andre ; Oliveira, Thiego ; Neto, Fernando B. de Lima ; Braga-Neto, Ulisses ; Bigham, Abigail W. ; Bamshad, Michael ; Marques, Ernesto T. A. ; Acioli-Santos, Bartolomeu

United States: IEEE

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