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Scopus İndeksli Yayınlar Koleksiyonu

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

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  • Conference Object
    Leveraging MicroRNA-Gene Associations With Mirgedinet: An Intelligent Approach for Enhanced Classification of Breast Cancer Molecular Subtypes
    (Springer International Publishing AG, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    Understanding the molecular subtypes of breast cancer is crucial for advancing targeted therapies and precision medicine. For the BRCA molecular subtype prediction problem, this study employs miRGediNET, a machinelearning approach that integrates data from miRTarBase, DisGeNET, and HMDD databases to investigate shared gene associations between microRNA (miRNA) activity and disease mechanisms. Using the BRCA LumAB_Her2Basal dataset, we evaluate miRGediNET's performance against traditional feature selection methods, including CMIM, mRmR, Information Gain (IG), SelectKBest (SKB), Fast Correlation-Based Filter (FCBF), and XGBoost (XGB). These feature selection techniques were assessed using various classification algorithms including Random Forest (RF), Support Vector Machine (SVM), LogitBoost, Decision Tree, and AdaBoost, all executed with default parameters. The feature selection methods were tested using Monte Carlo Cross-Validation, where performance metrics obtained for each iteration were averaged to ensure robustness. Our findings reveal that miRGediNET outperforms traditional methods in accuracy and Area Under the Curve (AUC), emphasizing its superior capability to identify key genes that bridge miRNA interactions and breast cancer mechanisms. Notably, both miRGediNET and Information Gain (IG) feature selection consistently identified ESR1, a critical biomarker frequently reported in recent research associated with breast cancer prognosis and resistance to endocrine therapies. This integrative approach provides deeper biological insights into miRNA-disease interactions, paving the way for enhanced patient stratification, biomarker discovery, and personalized medicine strategies. The miRGediNET tool, developed on the KNIME platform, offers a practical resource for further exploration in the field of bioinformatics and oncology.
  • Conference Object
    Citation - Scopus: 5
    Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Jabeer, Amhar; Kocak, Aysegul; Akkaş, Huseyin; Yenisert, Ferhan; Nalbantoĝlu, Özkan Ufuk; Yousef, Malik; Bakir-Güngör, Burcu; Bakir Gungor, Burcu
    A variety of bacterial species called gut microbiota work together to maintain a steady intestinal environment. The gastrointestinal tract contains tremendous amount of different species including archaea, bacteria, fungi, and viruses. While these organisms are crucial immune system stabilizers, the dysbiosis of the intestinal flora has been related to gastrointestinal disorders including Colorectal cancer (CRC), intestinal cancer, irritable bowel syndrome and inflammatory bowel disease. In the last decade, next-generation sequencing (NGS) methods have accelerated the identification of human gut flora. CRC is a deathly condition that has been on the rise in the last century, affecting half a million people each year. Since early CRC diagnosis is critical for an effective treatment, there is an immediate requirement for a classification system that can expedite CRC diagnosis. In this study, via analyzing the available metagenomics data on CRC, we aim to facilitate the CRC diagnosis via finding biomarkers linked with CRC, and via building a classification model. We have obtained the metagenomic sequencing data of the healthy individuals and CRC patients from a metagenome-wide association analysis and we have classified this data according to the disease stages. Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), Extreme Gradient Boosting (XGBoost), min redundancy max relevance (mRMR), Information Gain (IG) and Select K Best (SKB) feature selection algorithms were utilized to cope with the complexity of the features. We observed that the SKB, IG, and XGBoost techniques made significant contributions to decrease the microbiota in use for CRC diagnosis, thereby reducing cost and time. We realized that our Random Forest classifier outperformed Adaboost, Support Vector Machine, Decision Tree, Logitboost and stacking ensemble classifiers in terms of CRC classification performance. Our results reiterated some known and some potential microbiome associated mechanisms in CRC, which could aid the design of new diagnostics based on the microbiome. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 2
    Classification of Breast Cancer Molecular Subtypes With Grouping-Scoring Approach That Incorporates Disease-Disease Association Information
    (IEEE, 2024-05-15) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    This study uses modern sequencing technology and large biological databases to investigate the molecular intricacies of complicated diseases like cancer. Using gene expression databases and biomarkers, the research aims to improve breast cancer molecular subtype identification for better patient outcomes. Using BRCA LumAB_ Her2Basal dataset, this study compares an integrative machine learning-based strategy (GediNET) to traditional feature selection approaches across machine learning classifiers. GediNET excels at uncovering crucial disease-disease connections and potential biomarkers using the Grouping-Scoring-Modeling (GSM) approach, which favors gene groupings above individual genes. Our comparative analysis highlights GediNET's exceptional performance, notably in terms of accuracy and Area Under the Curve metrics, underscoring its effectiveness in uncovering the genetic intricacies of breast cancer. GediNET's promise to improve disease classification and biomarker identification by improving biological mechanism understanding goes beyond exceeding traditional approaches. The work shows that GediNET's integrative method can promote bioinformatics research by identifying the most informative genes associated with certain diseases, enabling focused and customized medicine.