Prediction and classification of diabetes mellitus using genomic data
Intelligent IoT Systems in Personalized Health Care, Elsevier, ss.235-292, 2020
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2020
- Doi Numarası: 10.1016/b978-0-12-821187-8.00009-5
- Yayınevi: Elsevier
- Sayfa Sayıları: ss.235-292
- Anahtar Kelimeler: Classification, Deep neural networks, Diabetes mellitus, Genetic algorithm, Genomic data
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
Diabetes mellitus (DM) is one of the chronic and debilitating diseases in modern society, hence the urgent need to prevent epidemic growth in society. This chapter is motivated by the studies of several scholars in the field of microarrays datasets for gene expression. Nonetheless, there are very few available gene signatures across datasets, thereby generating sample selection bias and over selection sets matching. Subjective selection of this gene and sample pairings could be addressed through large average submatrices and a unique method of biclustering using objective statistical assumptions to reconstruct robust signatures of expression. Hence, SWITCH (SupWald Identification of CHanges DNA copy) was created to label CNAs in platforms of aCGH and to connect them with subtypes. Therefore, the process of selecting the most informative gene biomarker was done using the genetic algorithm (GA) and deep neural networks (DNN) for biological sample classification. The simulated genomics datasets were divided into 95% training and 5% test samples and the DNN classifier is modified using these sets of SNPs and fine-tuned to classify type II DM analyses. The datasets are cleaned into four single-nucleotide polymorphism (SNP) function sets: 96 (P-value: 1 × 10-5), 214 (P-value: 1 × 10-4), 399 (P-value: 1 × 10-3), and 678 (P-value: 1 × 10-2) using P-value thresholds. The classifier was built using the training data while testing its efficiency on the test sample. MATLAB was used to implement the GA and DNN. The DNN model showed a significant predictive output with type II DM having AUC = 0.9537 in male and AUC = 0.9349 in female.