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 SebnemHas files: No

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  • Conference Object
    Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning
    (IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, Burcu
    The rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.
  • Conference Object
    In-silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms
    (IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019-04) Ersoz, Nur Sebnem; Guzel, Yasin; Bakir-Gungor, Burcu
    Representing approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified.
  • Conference Object
    Metagenomic Data Analysis With Machine Learning to Discover Colorectal Cancer-Associated Enzymes
    (IEEE, 2024-05-15) Ersoz, Nur Sebnem; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, Burcu
    The human gut microbiome comprises over 10 trillion microbes and plays important roles in maintaining metabolism, body homeostasis, impacting immune function. Metagenomics which studies genomic data from clinical and environmental samples is crucial in understanding the interplay between the host and the gut microbiome. Recently, functional profiling of metagenomes helps to identify alterations in microbial functions, particularly enzyme-encoding genes. Colorectal cancer (CRC) is known as one of the leading causes of cancer-related deaths. In this study, we aimed to find the CRC-associated enzymes by analyzing metagenomic data with different machine learning methods. A total of 1262 samples including CRC and control groups from different countries were used in this study. This dataset was obtained by functionally profiling metagenomics data and estimating community level enzyme commission (EC) abundance values. For the analysis of this dataset, RCE-IFE and SVM-RCE machine learning methods, which are group-based feature selection methods, were compared with 6 different individual feature selection methods. 10 times Monte-Carlo Cross Validation was used in our experiments. It was observed that RCE-IFE, Extreme Gradient Boosting and Select K Best methods similarly provided the best performances. Especially in this study, besides the its high performance, the group-based feature selection method RCE-IFE grouped enzymes into clusters unlike TFS, and then identified biologically relevant CRC-associated enzymes.
  • 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.