Impact of Bioinformatics, Genomic Data and Disease Prediction

Abstract

The combination of high throughput genomic sequencing, bioinformatics tools and machine learning algorithms has opened up new possibilities in the fields of personalized therapeutic strategies, early diagnosis and disease prediction. In this study, a quantitative research design using secondary data analysis is used to examine how the integration of bioinformatics and genomic data can affect the accuracy and effectiveness of disease prediction models in three diseases: cardiovascular diseases, type 2 diabetes and cancer. Genomic datasets from the public were analyzed with well-established bioinformatics pipelines, involving sequence alignment (BWA), variant calling (GATK), gene expression analysis (DESeq2) and predictive modeling using machine learning algorithms such as random forest, support vector machines (SVM), deep neural networks (DNN) and gradient boosting. The accuracy, precision, recall, F1-score and area under receiver operating characteristic (ROC) curve (AUC) were used to assess model performance. The performance of the deep neural network model is the highest with regard to disease prediction accuracy (92.1%) and AUC (0.96) in the combined datasets, which was followed by gradient boosting (91.2%, AUC = 0.95). The results presented show that the accuracy of disease prediction using the bioinformatics approach to genomic analysis is significantly improved when compared to the traditional clinical risk models, and the largest improvements are seen in cancer and cardiovascular disease prediction. This study shows that multi-omics data integration, feature selection and model validation are essential for the progress of precision medicine and predictive healthcare outcomes.