Data Mining Techniques In Bioinformatics Enhanced by HPC and AI Convergence

High-Performance Computing (HPC) and Artificial Intelligence (AI) have come together to usher in a new age of data mining in the field of bioinformatics. Using high-performance computing (HPC) and artificial intelligence (AI), this research presents a suggested strategy for improving data mining techniques and then evaluates it alongside six more conventional approaches. Discovering latent patterns in high-dimensional biological data is the goal of the proposed technique, which combines Principal Component Analysis (PCA), Convolutional Neural Networks (CNN), and Random Forest (RF). The suggested technique consistently outperforms the state-of-the-art methods in our comparisons, including in terms of accuracy, precision, recall, and F1 score. These performance criteria are essential for effective data mining in bioinformatics, and the system's shown equilibrium between them is rather impressive. The suggested strategy also drastically decreases execution time, which is a crucial aspect in effectively managing large-scale biological information. Our findings highlight the need for AI-driven data mining methods in the field of bioinformatics. The application of PCA offers dimensionality reduction, assisting in feature selection and boosting model performance. When it comes to classifying and ranking the value of features, RF provides ensemble learning capabilities, whereas CNNs excel at extracting complicated patterns from pictures and sequences. The suggested technique is holistic in nature, drawing on the advantages of several algorithms to improve data mining and provide a complete service to the field of biological research. Our results show that the suggested approach has considerable potential to further bioinformatics studies and applications. It helps scientists speed up drug development, improve the accuracy of diagnostics, and unearth new biological insights. In addition, the faster execution time suggests better use of computer resources, which is useful for making decisions and analyses in the life sciences and healthcare in real time.