ABSTRACT
Machine learning (ML) has become a powerful tool in real-life applications including modern biology. The relationship between biology and ML has gained widespread attention in various facets of biological engineering. The chapter first introduces ML and its comparison with other computing environments. The various approaches to ML such as the decision tree, deep learning, support of vector machines, clustering, Bayesian networks, and mining methods are discussed. Data mining with different techniques and related analysis are also then discussed. Finally, the importance of big data and various implementation frameworks and applications in biology are elaborated in detail. In can be concluded that ML along with human intervention can prove to be a powerful tool for many aspects of biological engineering, as it can alleviate the burden of solving many biological problems and save the time and cost required for new experiments and provide predictions to guide new experiments.
