Research Information System
Thesis Type: Doctorate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Graduate School of Informatics, Medical Informatics, Turkey
Approval Date: 2018
Student: ARİF YILMAZ
Supervisor: YEŞİM AYDIN SON
Abstract:Single Nucleotide Polymorphisms (SNP) holds a promise in identification of genomic footprints of complex diseases such as cancer and diabetes. However identification of SNPs associated to complex diseases is a challenging problem due to the high number and variety of SNPs present in individual genomes. Analysis of genome wide studies of SNP datasets mainly focus on statistical evidence. As there are close to hundred million SNPs in human genome, incorporating biological and functional knowledge about statistically significant SNPs provides valuable features for further selection of SNPs. Analytical Hierarchy Process (AHP) based SNP prioritization approach is a method developed for this purpose. However, AHP requires expert knowledge, which results in subjective decisions. In this work, we propose a novel approach for AHP design and optimization by utilizing Random Forest based AHP (RF-AHP) assessment on categories. We utilized the results of previously developed genomic model on Prostate Cancer. Proposed RF-AHP approach was compared with Delphi-AHP based method on Schizophrenia, Prostate Cancer, Type 2 Diabetes and Alzheimer’s disease genomic datasets and same performance was achieved. Additionally, RegulomeDB database was integrated to RF-AHP. While similar performance was obtained in most of the datasets better prioritization scoring is achieved for Schizophrenia disease.