Hugh Cartwright (editor) - Artificial Neural Networks (Methods in Molecular Biology, 2190)

For further volumes: http://www.springer.com/series/7651 For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by-step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

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Two decades ago it would have been hard to foresee the remarkable growth in the use of artificial intelligence (AI) in the physical and life sciences. But there is a simple explanation for that rise: AI tools work.

Software is now readily available for Artificial Neural Networks, Genetic Algorithms, Deep Learning, Random Forests, Support Vector Machines, and other methods. While the software is not always trivial to use, it is becoming both more user-friendly and more powerful; this is encouraging scientists, whatever their specialization, to dive in.

This book showcases some of the studies now being pursued in the life sciences: topics range from the identification of genotype-phenotype correlations to the use of machine learning to evaluate biomedical time series; from de novo drug design to using recursive neural networks in the scoring of protein models; from studies of gene regulation in bacteria to the application of machine learning to the assessment of tumor tissue, and many more.

Traditional methods of analysis are in no imminent danger of being pushed out of the door by AI. On the contrary, these well-tested methods are increasingly being combined with the newer computational tools to enhance understanding of the large and complex datasets that the life sciences can generate.

As in earlier editions, readers who are intrigued by the applications discussed in these chapters will find practical details to help them apply the methods of AI in their own work.