Predicting the Disease of Alzheimer With SNP Biomarkers and Clinical Data Using Data Mining Classification Approach: Decision Tree

Erdogan, Onur
Aydın Son, Yeşim
Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.


Predicting the disease of Alzheimer (AD) with SNP biomarkers and clinical data based decision support system using data mining classification approaches
Erdoğan, Onur; Aydın Son, Yeşim; Department of Health Informatics (2012)
Single Nucleotide Polymorphisms (SNPs) are the most common DNA sequence variations where only a single nucleotide (A, T, C, G) in the human genome differs between individuals. Besides being the main genetic reason behind individual phenotypic differences, SNP variations have the potential to exploit the molecular basis of many complex diseases. Association of SNPs subset with diseases and analysis of the genotyping data with clinical findings will provide practical and affordable methodologies for the predi...
Integration of METU-SNP databases via RDF for PI_SNP web service
Gedikoğlu, Ceyhun; Aydın Son, Yeşim; Çarkacıoğlu, Levent; Department of Bioinformatics (2014)
Single Nucleotide Polymorphism (SNP) is a variation which occurs after a nucleotide mutates between members of a species or paired chromosomes in DNA sequence. SNP data is especially important for identifying genetic variations underlying complex diseases. The need for collection and service of this data under a standard format and globally normalized and structured metadata that houses the structured SNP data is becoming more important while recent advances in high-throughput genotyping technologies are re...
Determination of performance parameters for AHP based single nucleotide polymorphism (SNP) prioritization approach on Alzheimer’s disease data
Kadıoğlu, Onat; Aydın Son, Yeşim; Department of Bioinformatics (2011)
GWAS mainly aim to identify variations associated with certain phenotypes or diseases. Recently the combined p-value approach is described as the next step after GWAS to map the significant SNPs to genes and pathways to evaluate SNP-gene-disease associations. Major bottleneck of standard GWAS approaches is the prioritization of statistically significant results. The connection between statistical analysis and biological relevance should be established to understand the underlying molecular mechanisms of dis...
A novel SVM-ID3 Hybrid Feature Selection Method to Build a Disease Model for Melanoma using Integrated Genotyping and Phenotype Data from dbGaP
Aydın Son, Yeşim (2014-09-03)
The relations between Single Nucleotide Polymorphism (SNP) and complex diseases are likely to be non-linear and require analysis of the high dimensional data. Previous studies in the field mostly focus on genotyping and effects of various phenotypes are not considered. To fill this gap a hybrid feature selection model of support vector machine and decision tree has been designed. The designed method is tested on melanoma. We were able to select phenotypic features such as moles and dysplastic nevi, and SNPs...
Evaluating Oxygen Tensions Related to Bone Marrow and Matrix for MSC Differentiation in 2D and 3D Biomimetic Lamellar Scaffolds
Sayin, Esen; Baran, Erkan Turker; Elsheikh, Ahmed; Mudera, Vivek; Cheema, Umber; Hasırcı, Vasıf Nejat (2021-04-01)
The physiological O-2 microenvironment of mesenchymal stem cells (MSCs) and osteoblasts and the dimensionality of a substrate are known to be important in regulating cell phenotype and function. By providing the physiologically normoxic environments of bone marrow (5%) and matrix (12%), we assessed their potential to maintain stemness, induce osteogenic differentiation, and enhance the material properties in the micropatterned collagen/silk fibroin scaffolds that were produced in 2D or 3D. Expression of ost...
Citation Formats
O. Erdogan and Y. Aydın Son, “Predicting the Disease of Alzheimer With SNP Biomarkers and Clinical Data Using Data Mining Classification Approach: Decision Tree,” 2014, vol. 205, Accessed: 00, 2020. [Online]. Available: