This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication, studies of Tuberculosis, gene signatures in breast cancer classification, the use of mass spectrometry in metabolite identification, visual navigation, and computer diagnosis.
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.
Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and practical, Artificial Neural Networks: Third Edition should be of value to all scientists interested in the hands-on application of ANNs in the biosciences.
1. Identifying Genotype–Phenotype Correlations via Integrative Mutation Analysis 2. Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning 3. Siamese Neural Networks: An Overview 4. Computational Methods for Elucidating Gene Expression Regulation in Bacteria 5. Neuroevolutive Algorithms Applied for Modeling Some Biochemical Separation Processes 6. Computational Approaches for De Novo Drug Design: Past, Present, and Future 7. Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology 8. Building and Interpreting Artificial Neural Network Models for Biological Systems 9. A Novel Computational Approach for Biomarker Detection for Gene Expression-Based Computer-Aided Diagnostic Systems for Breast Cancer 10. Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues 11. Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning 12. Predicting Host Phenotype Based on Gut Microbiome Using a Convolutional Neural Network Approach 13. Predicting Hot Spots Using a Deep Neural Network Approach 14. Using Neural Networks for Relation Extraction from Biomedical Literature 15. A Hybrid Levenberg–Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models 16. Secure and Scalable Collection of Biomedical Data for Machine Learning Applications 17. AI-Based Methods and Technologies to Develop Wearable Devices for Prosthetics and Predictions of Degenerative Diseases
Deep Neural Networks and Applications Название: Deep Neural Networks and Applications Автор: Ivan Stanimirovic Издательство: Arcler Press Год: 2020 Страниц: 260 Язык: английский...
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Artificial Neural Networks, 3rd Edition (2021) 
Название: Artificial Neural Networks
