İnal Gültekin, Güldal

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Gültekin, Güldal
Gultekin, G.
Güldal İnal Gültekin
Gultekin, İnal
Gultekin, Güldal
Güldal, İnal Gültekin
GULTEKiN Guldal İnal
Gültekin, G.
Guldal Inal GULTEKiN
Guldal I. Gultekin
Guldal İ. Gultekin
G.,İNal Gültekin
Güldal İ. Gültekin
Gultekin Guldal İnal
Gultekin, Guldal
Guldal Inal Gultekin
Gültekin, İnal
GULTEKiN Guldal Inal
G. I. GULTEKiN
Inal Gultekin G.
G. İ. GÜLTEKIN
G. İ. Gultekin
GÜLTEKIN Güldal İnal
G. İ. GULTEKiN
Inal Gültekin G.
Güldal İnal GÜLTEKIN
Gültekin Güldal İnal
G. Inal Gultekin
Gultekin, I.
Guldal İnal Gultekin
Gültekin, İ.
İNal Gültekin, Güldal
Guldal İnal GULTEKiN
G. İnal Gultekin
Gultekin, İ.
G. İ. Gültekin
Inal-Gültekin G.
Inal-Gultekin G.
G. I. Gultekin
G., İNal Gültekin
G. İnal Gültekin
Gultekin Guldal Inal
Gultekin, Inal
Inal-Gultekin, Guldal
Job Title
Prof.Dr.
Email Address
guldal.inal@okan.edu.tr
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Scholarly Output

3

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3

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    Article
    Citation Count: 1
    Prediction of Alzheimer's Disease by a Novel Image-Based Representation of Gene Expression
    (Mdpi, 2022) İnal Gültekin, Güldal; Kalkan, Habil; Akkaya, Umit Murat; Inal-Gultekin, Guldal; Sanchez-Perez, Ana Maria; Fizyoloji / Physiology; Fizyoloji / Physiology
    Early intervention can delay the progress of Alzheimer's Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes' expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection.
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    Article
    Citation Count: 0
    Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways
    (Springer, 2022) Gultekin, Guldal Inal; İnal Gültekin, Güldal; Kahraman, Ozlem Timirci; Isbilen, Murat; Durmus, Saliha; Cakir, Tunahan; Yaylim, Ilhan; Isbir, Turgay; Fizyoloji / Physiology
    Background: The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders. Methods: The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples. Results: Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples. Conclusion: This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.
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    Article
    Citation Count: 3
    Defining Molecular Treatment Targets for Bladder Pain Syndrome/Interstitial Cystitis: Uncovering Adhesion Molecules
    (Frontiers Media Sa, 2022) İnal Gültekin, Güldal; Inal-Gultekin, Guldal; Gormez, Zeliha; Mangir, Naside; Fizyoloji / Physiology; Fizyoloji / Physiology
    Bladder pain syndrome/interstitial cystitis (BPS/IC) is a debilitating pain syndrome of unknown etiology that predominantly affects females. Clinically, BPS/IC presents in a wide spectrum where all patients report severe bladder pain together with one or more urinary tract symptoms. On bladder examination, some have normal-appearing bladders on cystoscopy, whereas others may have severely inflamed bladder walls with easily bleeding areas (glomerulations) and ulcerations (Hunner's lesion). Thus, the reported prevalence of BPS/IC is also highly variable, between 0.06% and 30%. Nevertheless, it is rightly defined as a rare disease (ORPHA:37202). The aetiopathogenesis of BPS/IC remains largely unknown. Current treatment is mainly symptomatic and palliative, which certainly adds to the suffering of patients. BPS/IC is known to have a genetic component. However, the genes responsible are not defined yet. In addition to traditional genetic approaches, novel research methodologies involving bioinformatics are evaluated to elucidate the genetic basis of BPS/IC. This article aims to review the current evidence on the genetic basis of BPS/IC to determine the most promising targets for possible novel treatments.