Browsing by Author "Bakir Gungor, Burcu"

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A Comparative Study on Psychiatric Disorders: Identification of Shared Pathways and Common Agents
(Institute of Electrical and Electronics Engineers Inc., 2022) Kuzudisli, Cihan; Bakir-Güngör, Burcu; Bakir Gungor, Burcu
Distinct but closely related diseases generally present shared symptoms, which address possible overlaps among their pathogenic mechanisms. Identification of significantly impacted shared pathways and other common agents are expected to elucidate etiology of these disorders and to help design better intervention strategies. In this research effort, we studied six psychiatric disorders including schizophrenia (SCZ), anorexia (AN), bipolar disorder (BD), depressive disorder (DD), autism (AU) and attention deficit hyperactivity disorder (ADHD). Our methodology can be classified into the following two parts: In Part I, common susceptibility genes; and in Part II, genome-wide association studies (GWAS) data were used to find enriched pathways of psychiatric disorders. 59 KEGG pathways were commonly identified in both parts. 31 of these pathways are disease pathways. Pathways related to cancer and infectious diseases were predominant compared to others. Most of the acquired pathways were in accordance with previous studies in literature. A combination of susceptibility genes and GWAS data is an effective approach to identify significantly impacted pathways in multifactorial diseases. In this respect, shared modules were determined after applying hierarchical clustering of the enriched pathways. These identified modules may tell us the association of psychiatric disorders with the enriched pathways. Taken all together, common pathways and shared modules are expected to highlight the causative factors and important mechanisms behind complex psychiatric diseases, leading to effective drug discovery. © 2022 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Correlation of PAPP-A Values With Maternal Characteristics, Biochemical and Ultrasonographic Markers of Pregnancy
(Marmara Univ, Fac Medicine, 2021) Kaymakcalan, Hande; Uzut, Ommu Gulsum; Harkonen, Juho; Bakir Gungor, Burcu; Bakir-gungor, Burcu; Söylemez, Ümmü Gülsüm
Objective: Our aim is to investigate whether there is a correlation of pregnancy-associated plasma protein A (PAPP-A) values with other variables in pregnancy and maternal characteristics. Materials and Methods: We retrospectively analyzed the relation between the PAPP-A levels, demographics, biochemical and ultrasonographic markers of the first trimester screening of 11,842 pregnant women seen at a tertiary hospital between November 2002 and November 2008. Results: A significant difference between PAPP-A values of the diabetic and non-diabetic pregnant women were observed (p=0.0005, Mann-Whitney U test). In terms of weight, crown-rump length, Beta-hCG values, significant differences were observed between low and medium level PAPP-A subgroups and between low and high level PAPP-A subgroups. PAPP-A levels were found to differ significantly between the pregnant women of Caucasian origin and other racial origins. Conclusions: Pregnant women with different ethnic and medical backgrounds have different PAPP-A values and other markers of the aneuploidy screening. 'lb make patient specific risk predictions, understanding these interactions and differences is important. Future studies are needed to understand the pathopyhsiology behind these differences.
Evaluation of Sub-Network Search Programs in Epilepsy-Related GWAS Dataset
(Pamukkale Univ, 2022) Adanur Dedeturk, Beyhan; Bakir Gungor, Burcu; Dedeturk, Beyhan Adanur; Gungor, Burcu Bakir
The active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.
Citation - Scopus: 5
Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods
(Institute of Electrical and Electronics Engineers Inc., 2022) Jabeer, Amhar; Kocak, Aysegul; Akkaş, Huseyin; Yenisert, Ferhan; Nalbantoĝlu, Özkan Ufuk; Yousef, Malik; Bakir-Güngör, Burcu; Bakir Gungor, Burcu
A variety of bacterial species called gut microbiota work together to maintain a steady intestinal environment. The gastrointestinal tract contains tremendous amount of different species including archaea, bacteria, fungi, and viruses. While these organisms are crucial immune system stabilizers, the dysbiosis of the intestinal flora has been related to gastrointestinal disorders including Colorectal cancer (CRC), intestinal cancer, irritable bowel syndrome and inflammatory bowel disease. In the last decade, next-generation sequencing (NGS) methods have accelerated the identification of human gut flora. CRC is a deathly condition that has been on the rise in the last century, affecting half a million people each year. Since early CRC diagnosis is critical for an effective treatment, there is an immediate requirement for a classification system that can expedite CRC diagnosis. In this study, via analyzing the available metagenomics data on CRC, we aim to facilitate the CRC diagnosis via finding biomarkers linked with CRC, and via building a classification model. We have obtained the metagenomic sequencing data of the healthy individuals and CRC patients from a metagenome-wide association analysis and we have classified this data according to the disease stages. Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), Extreme Gradient Boosting (XGBoost), min redundancy max relevance (mRMR), Information Gain (IG) and Select K Best (SKB) feature selection algorithms were utilized to cope with the complexity of the features. We observed that the SKB, IG, and XGBoost techniques made significant contributions to decrease the microbiota in use for CRC diagnosis, thereby reducing cost and time. We realized that our Random Forest classifier outperformed Adaboost, Support Vector Machine, Decision Tree, Logitboost and stacking ensemble classifiers in terms of CRC classification performance. Our results reiterated some known and some potential microbiome associated mechanisms in CRC, which could aid the design of new diagnostics based on the microbiome. © 2022 Elsevier B.V., All rights reserved.