WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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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, MalikUnderstanding 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 - WoS: 2Citation - Scopus: 2Classification 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, MalikThis 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.