Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Colorectal Cancer Prediction via Applying Recursive Cluster Elimination With Intra-Cluster Feature Elimination on Metagenomic Pathway Data(Springer International Publishing AG, 2024) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, BurcuAdvances in next-generation sequencing and in "-omics" technologies enable the characterization of the human gut microbiome. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental influences, and abnormalities in the gut microbiota. The aim of this study is to identify pathways that influence host metabolism in CRC patients. The CRC-related metagenomic dataset used in this study contains the relative abundance values of 551 pathways calculated for 1262 samples. Here, two different approaches based on the feature grouping reduce the number of features by considering relevant features as groups, eliminate irrelevant features, and perform classification. The recursive cluster elimination with intra-cluster feature elimination (RCE-IFE) approach achieves anAUCof 0.72 using an average of 66.2 features on CRC-associated metagenomics dataset. In these experiments, P163-PWY: L-lysine fermentation to acetate and butanoate and PWY-6151: S-adenosyl-L-methionine cycle I pathways are identified as potential biomarkers associated with CRC. These experiments also reduce the number of features reported by both approaches in P163-PWY: L-lysine fermentation to acetate and butanoate and PWY-6151: Sadenosyl-L-methionine cycle I pathways reported by both approaches are considered possible CRC-related biomarkers. This study contributes to the molecular diagnosis and treatment of colorectal cancer by revealing the pathways associated with CRC. Our results are promising for the study of the gut microbiota and its role in CRC.Article Citation - Scopus: 5CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques(Elsevier Ltd, 2024-11) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, MalikColorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.