Machine Learning In Bioinformatics: Disease Prediction Models

Abstract

This paper discuss machine learning applied in bioinformatics, particularly focusing on models to predict the occurrence of diseases using disease diagnosis with improved accuracy and with the possibility of biomarkers for heart disease, Alzheimer's, and cancer among others. Utilizing deep neural networks, hypergraph learning, and transformer-based methods, models are assessed against datasets developed from genetic sequences and medical records. Among the most important findings is a 15% improvement in predictive accuracy for the prediction of miRNA-disease association and 12% reduction in false positives using a transformer-based model, particularly to detect heart disease, while selecting genes by feature screening in Alzheimer studies showed specificity at 91%, thus providing a means of accurately identifying genetic markers. In summary, this works of research exemplify the effectiveness of hybrid techniques that combine the power of digital twins and ensemble learning for dependable data and potentially large-scale generalizability across models. In the future, optimization towards real time applications and wider use cases in precision medicine are envisioned. The contributions of the study affirm the vital role machine learning plays in predictive bioinformatics - a pathway to further accurate diagnostics and personalized treatment plans.