Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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, BurcuThe 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 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, BurcuThe 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.