Abstract
The fusion of bioinformatics and machine learning (ML) has brought forth a new era of innovative healthcare. The various applications of machine learning in bioinformatics and healthcare are examined in this chapter, with an emphasis on how revolutionary these applications could be for disease identification, drug development, and customized therapy. Because of its ability to find intricate patterns in large datasets, machine learning has emerged as a key component of bioinformatics study. Machine learning algorithms are used in genomics to decipher the human genome, identify genetic variants associated with diseases, and predict an individual’s susceptibility to inherited disorders. Targeted medicines and individualized healthcare interventions are being made possible by this fresh understanding. Machine learning (ML) is a critical component of drug research and development since it helps identify promising drug candidates, speeds up chemical compound screening, and improves clinical trial designs. By employing data-driven insights and predictive modeling, machine learning (ML) is bringing innovative drugs to market faster and cheaper, which will eventually benefit patients all around the world. Moreover, ML-driven healthcare uses extend beyond the creation of new drugs and genomes. Machine learning is increasing the precision and effectiveness of healthcare delivery in a variety of domains, including patient risk assessment, therapeutic recommendation, and diagnostic assistance systems. These AI-powered tools enhance patient outcomes, lower diagnostic error rates, and enable healthcare professionals to make wiser decisions.
