Hybrid Bioinformatics and Clinical Data Integration Model for Biomarker Discovery and Disease Prediction

Abstract

The blistering growth of biomedical and clinical information produced by genomics, proteomics, transcriptomics and electronic health records (EHRs) has profoundly changed the contemporary computational medicine and predictive healthcare systems. Nonetheless, these datasets are very heterogeneous and high-dimensional, which poses great challenges in integrating the data, extracting the features, identifying the biomarkers, and being able to accurately predict the disease. This paper presents a Hybrid Bioinformatics and Clinical Data Integration Model of Biomarker Discovery and Disease Prediction which will integrate information of molecular-level bioinformatics and patient-based clinical data to enhance the diagnostic accuracy and predictive power. The innovative framework combines multi-omics datasets, laboratory reports, demographic features, and clinical histories with the help of enhanced preprocessing and normalization of the data and features fusion tools. Biomarker selection and disease classification are done using machine learning algorithms such as Random Forest, Support Vector Machine (SVM) and XGBoost. The dimensionality reduction and statistical filtering method are used in order to remove redundant features and enhance the computational efficiency. Experimental analysis shows that the hybrid integration model has better accuracy, sensitivity, specificity, recall, and F1-score than traditional single-source prediction models. The framework is able to identify clinically relevant biomarkers with disease progression and early disease diagnosis, prediction and treatment planning. The suggested model will provide intelligent and scalable health care analytics, translational medicine, AI-based clinical decision support systems.