Abstract
Predictive modeling in bioinformatics is a powerful approach to understanding complex biological data and making predictions based on that data. By applying machine learning and statistical techniques, predictive models help in tasks such as disease prediction, drug discovery, biomarker identification, and functional genomics. This article provides an overview of the tools and techniques used in predictive modeling in bioinformatics, including regression analysis, support vector machines, neural networks, and ensemble methods. We also discuss the challenges in building predictive models for bioinformatics applications and the future trends in this rapidly evolving field.
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