Metabolic Syndrome Risk Evaluation Based on VDR Polymorphisms and Neural Networks
| Autor: | Adnan Khashman, Nedime Serakinci, Meral Kizilkanat |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
medicine.medical_specialty Artificial neural network business.industry 02 engineering and technology Bioinformatics medicine.disease Calcitriol receptor Risk evaluation 020901 industrial engineering & automation Polymorphism (computer science) Genetic variation 0202 electrical engineering electronic engineering information engineering medicine Vitamin D and neurology Medical genetics lipids (amino acids peptides and proteins) 020201 artificial intelligence & image processing Metabolic syndrome business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030352486 |
| DOI: | 10.1007/978-3-030-35249-3_126 |
| Popis: | The paper presents an intelligent implementation in medical genetics that supports clinical and laboratory practices by evaluating the risk of having metabolic syndrome (MetS) disorder based on its association with genetic variations or polymorphisms in Vitamin D Receptors (VDR). MetS is approximated in this work with irregularities in biochemical measurements of cholesterol and triglyceride levels in patients. The arbitration of this non-linear relation between VDR polymorphism and metabolic disorders is performed using a backpropagation neural network. The development of this risk evaluation system uses a dataset of biochemical and genetic data of 165 anonymous patients. The experimental results suggest that machine artificial neural networks can be successfully employed to evaluate the risk of metabolic syndrome using genetic and biochemical information. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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