Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Ersoz, Nur SebnemCOAR Access Rights: search.filters.coarAccessRights.open access

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Machine Learning-Based Prediction of Autism Spectrum Disorder and Discovery of Related Metagenomic Biomarkers With Explainable AI
    (MDPI, 2025-08-21) Temiz, Mustafa; Bakir-Gungor, Burcu; Ersoz, Nur Sebnem; Yousef, Malik
    Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation.
  • Article
    Integrating Biological Domain Knowledge With Machine Learning for Identifying Colorectal-Cancer Microbial Enzymes in Metagenomic Data
    (MDPI, 2025-03-08) Bakir-Gungor, Burcu; Ersoz, Nur Sebnem; Yousef, Malik
    Advances in metagenomics have revolutionized our ability to elucidate links between the microbiome and human diseases. Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, has been associated with dysbiosis of the gut microbiome. This study aims to develop a method for identifying CRC-associated microbial enzymes by incorporating biological domain knowledge into the feature selection process. Conventional feature selection techniques often evaluate features individually and fail to leverage biological knowledge during metagenomic data analysis. To address this gap, we propose the enzyme commission (EC)-nomenclature-based Grouping-Scoring-Modeling (G-S-M) method, which integrates biological domain knowledge into feature grouping and selection. The proposed method was tested on a CRC-associated metagenomic dataset collected from eight different countries. Community-level relative abundance values of enzymes were considered as features and grouped based on their EC categories to provide biologically informed groupings. Our findings in randomized 10-fold cross-validation experiments imply that glycosidases, CoA-transferases, hydro-lyases, oligo-1,6-glucosidase, crotonobetainyl-CoA hydratase, and citrate CoA-transferase enzymes can be associated with CRC development as part of different molecular pathways. These enzymes are mostly synthesized by Eschericia coli, Salmonella enterica, Klebsiella pneumoniae, Staphylococcus aureus, Streptococcus pneumoniae, and Clostridioides dificile. Comparative evaluation experiments showed that the proposed model consistently outperforms traditional feature selection methods paired with various classifiers.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 21
    GeNetOntology: Identifying Affected Gene Ontology Terms via Grouping, Scoring, and Modeling of Gene Expression Data Utilizing Biological Knowledge-Based Machine Learning
    (Frontiers Media S.A., 2023-08-21) Ersoz, Nur Sebnem; Bakir-Gungor, Burcu; Yousef, Malik
    Introduction: Identifying significant sets of genes that are up/downregulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of diseases. In order to address this problem, biological domain knowledge-based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge-based methods take the underlying biology into account and integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, and biological process of the gene product.Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype.Results: Our approach has been tested on 11 different gene expression datasets, and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model.Discussion: GeNetOntology will assist geneticists and scientists to identify a range of disease-related genes and ontologies in transcriptomic data analysis, and it will also help doctors design diagnosis platforms and improve patient treatment plans.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Differential in Vitro Anti-Leukemic Activity of Resveratrol Combined With Serine Palmitoyltransferase Inhibitor Myriocin in FMS-Like Tyrosine Kinase 3-Internal Tandem Duplication (FLT3-LTD) Carrying AML Cells
    (Springer, 2022-02-14) Ersoz, Nur Sebnem; Adan, Aysun
    Treatment of FMS-like tyrosine kinase 3 (FLT3)-internal tandem duplication (ITD) AML is restricted due to toxicity, drug resistance and relapse eventhough targeted therapies are clinically available. Resveratrol with its multi-targeted nature is a promising chemopreventive remaining limitedly studied in FLT3-ITD AML regarding to ceramide metabolism. Here, its cytotoxic, cytostatic and apoptotic effects are investigated in combination with serine palmitoyltransferase (SPT), the first enzyme of de novo pathway of ceramide production, inhibitor myriocin on MOLM-13 and MV4-11 cells. We assessed dose-dependent cell viability, flow cytometric cell death and cell cycle profiles of resveratrol in combination with myriocin by MTT assay, annexin-V/PI staining and PI staining respectively. Resveratrol's dose-dependent effect on SPT protein expression was also checked by western blot. Resveratrol decreased cell viability in a dose- dependent manner whereas myriocin did not affect cell proliferation effectively in both cell lines after 48h treatments. Although resveratrol induced both apoptosis and a significant S phase arrest in MV4-11 cells, it triggered apoptosis and non-significant S phase accumulation in MOLM-13 cells. Co-administrations reduced cell viability. Increased cytotoxic effect of co-treatments was further proved mechanistically through induction of apoptosis via phosphatidylserine relocalization. The cell cycle alteration in co-treatment was significant with an S phase arrest in MV4-11 cells, however, it was not effective on cell cycle progression of MOLM-13 cells. Resveratrol also increased SPT expression. Overall, modulation of SPT together with resveratrol might be the possible explanation for resveratrol's action. It could be an integrative medicine for FLT3-ITD AML after investigating its detailed mechanism of action in relation to de novo pathway of ceramide production.