Browsing by Author "Ayesha Karim"

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    An intelligent diagnostic model to predict disease associated biomarkers in genomic sequences
    (UMT, Lahore, 2025) Ayesha Karim
    Objective: Cell mutation refers to changes in the genetic material (DNA or RNA) of a cell that can disrupt normal protein synthesis and cell function. While some mutations have minimal effect, others can lead to the production of abnormal or dysregulated proteins, causing disruptions like genetic disorder. The objective of this study is to develop a computational model that predicts driver genes causing such disruptions in body in the early stages using genomic data, aiming to enhance early diagnosis and intervention. Methods: This study utilized a benchmark genomic dataset, which was processed using feature extraction techniques to identify relevant genetic patterns. Several ensemble classification methods, including XGBoost, Random Forest, LightGBM, ExtraTrees, Bagging, and a stacked ensemble of classifiers, were applied to assess the predictive power of the genomic features. The model, eNSMBL-PRED, was rigorously validated using multiple performance metrics such as accuracy, sensitivity, specificity, and Mathew’s correlation coefficient. Results: The proposed model demonstrated superior performance across various validation techniques. The self-consistency test achieved 100% accuracy, while the independent set and cross-validation tests yielded 96% and 96% accuracy, respectively. These results highlight the model's robustness and reliability in predicting Genetic disorder-related genes. Conclusion: The eNSMBL-PRED model provides a promising tool for the early detection of genetic biomarkers associated with the disorder. In the future, this model has the potential to assist healthcare professionals, particularly doctors, and psychologists, in diagnosing and formulating treatment plans for Genetic Disorder at its earliest stages