IEEE transactions on biomedical engineering, 2019-10, Vol.66 (10), p.2861-2868
2019
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- Autor(en) / Beteiligte
- Titel
- Severe Dengue Prognosis Using Human Genome Data and Machine Learning
- Ist Teil von
- IEEE transactions on biomedical engineering, 2019-10, Vol.66 (10), p.2861-2868
- Ort / Verlag
- United States: IEEE
- Erscheinungsjahr
- 2019
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0018-9294eISSN: 1558-2531DOI: 10.1109/TBME.2019.2897285Titel-ID: cdi_crossref_primary_10_1109_TBME_2019_2897285
- Format
- –
- Schlagworte
- 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)