Machine Learning Classification of Hemoglobin Beta Gene Mutations скачать в хорошем качестве

Machine Learning Classification of Hemoglobin Beta Gene Mutations

#HBBGene #VariantPathogenicity #MachineLearning #ProteinEncoding #XGBoost #Hemoglobinopathies #GenomicMedicine #Bioinformatics #PrecisionMedicine #ComputationalBiology Machine Learning Classification of Hemoglobin Beta Gene Mutations Anja Radomirovi c , University Union , Serbia Abstract: Mutations in the HBB gene cause severe hemoglobinopathies such as sickle cell disease and beta-thalassemia. Accurate HBB variant classification is crucial for diagnosis but remains challenging. I present a bioinformatics pipeline integrating HGVS parsing, Ensembl annotation, SpliceAI, and BioPython to analyze 1,809 ClinVar variants. Seven models were trained with SMOTE. XGBoost achieved an F1-score of 0.9495 and perfect recall, though ROC-AUC 0.4489 showed discrimination limits. Results highlight ML challenges for single-gene classification and importance of data quality in genomic medicine. Keywords: HBB gene, variant pathogenicity, machine learning, protein encoding, XGBoost, hemoglobinopathies Abstract URL : https://aircconline.com/csit/abstract... Article full text:https://aircconline.com/csit/papers/v... Volume URL : https://airccse.org/csit/V16N01.html Areas of Action for Process Automation in Drop Shipment Machine Learning Classification of Hemoglobin Beta Gene Mutations