Artificial Neural Networks: 3058

Further chapters focus on some of the AI methods themselves that lie at the heart of such applications, including machine learning and ensemble learning, biological networks, probabilistic neural networks, graph neural networks, and transformers. This fourth edition volume builds on the strengths of previous editions, with examples of how AI is being used in innovative ways to process, interpret, and understand biochemical and related data. Chapters cover a wide range of topics, from bacteriophage genomics, bioimage analysis, and the prediction of the properties of antibodies, to biomedical prediction, disease prevention, label-free imaging, and more. Further chapters focus on some of the AI methods themselves that lie at the heart of such applications, including machine learning and ensemble learning, biological networks, probabilistic neural networks, graph neural networks, and transformers. The emphasis throughout is on powerful methods that have been shown to be of proven value. Cutting-edge and authoritative, Artificial Neural Networks, Fourth Edition provides up-to-date information on the practical use of AI in bioscience, which and should be valuable in the planning of new bioscience projects.