PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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  • Article
    Citation - Scopus: 2
    Prediction of Colorectal Cancer Based on Taxonomic Levels of Microorganisms and Discovery of Taxonomic Biomarkers Using the Grouping-Scoring (G-S-M) Approach
    (Elsevier Ltd, 2025-03) Bakir-Güngör, Burcu; Temiz, Mustafa; Canakcimaksutoglu, Beyza; Yousef, Malik
    Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Evaluation of HOTAIR, HOXD8, HOXD9, HOXD11 Gene Expression Levels in Turkish Patients With Acute and Chronic Myeloid Leukemia: A Single Center Experience
    (Cellular and Molecular Biology Association, 2024-11-27) Saraymen, Esma; Erdem, Yakut; Akalin, Hilal Ünlü; Taşçıoğlu, Nazife; Saraymen, Berkay; Celik, Serhat; Özkul, Yusuf T.
    Homeobox (HOX) transcript antisense RNA (HOTAIR) and HOX genes are reported to be more expressed in various cancers in humans in recent studies. The role of HOTAIR and HOXD genes in acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) is not well known. In this study, expression levels of HOXD8, HOXD9 and HOXD11 from HOXD gene family and HOTAIR were determined from peripheral blood samples of 30 AML and 30 CML patients and 20 healthy volunteers by quantitative Real Time PCR. We determined that the expression levels of HOXD9 and HOXD11 in the AML patients were significantly lower than the control group (p<0.001 and p=0.002, respectively). There was no significant difference in the expression levels of HOTAIR and HOXD8 when compared to the control group. In the CML patients there was a significant increase in the expression level of HOTAIR when compared to the control group (p=0.002). The expression levels of HOXD9 and HOXD11 were found to be significantly lower than the control group (p<0.001). Our study showed that HOTAIR may not be a biomarker in the diagnosis and is not significantly correlated with the clinicopathological prognostic characteristics of AML. Additionally; it can be said that HOTAIR is oncogenic by suppressing the expression of HOXD9 and HOXD11 but not HOXD8 in CML patients. The expression profiles of HOTAIR may be a potential biomarker in the diagnosis of CML patients in predicting and monitoring drug resistance. © 2025 Elsevier B.V., All rights reserved.
  • Book Part
    Citation - Scopus: 4
    Computational Detection of Pre-MicroRNAs
    (Humana Press Inc., 2021-08-26) Saçar Demirci, Müşerref Duygu
    MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed. © 2021 Elsevier B.V., All rights reserved.