Artificial Neural Networks

Herausgegeben:Cartwright, Hugh

• Gebundenes Buch

Produktbeschreibung

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. 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.

Produktdetails

• Methods in Molecular Biology 2190
• Verlag: Humana / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-0716-0825-8
• 3. Aufl.
• Seitenzahl: 372
• Erscheinungstermin: 18. August 2020
• Englisch
• Abmessung: 260mm x 183mm x 26mm
• Gewicht: 896g
• ISBN-13: 9781071608258
• ISBN-10: 1071608258
• Artikelnr.: 59179716

Inhaltsangabe

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis.- Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning.- Siamese Neural Networks: An Overview.- Computational Methods for Elucidating Gene Expression Regulation in Bacteria.- Neuro-evolutive Algorithms Applied for Modeling Some Biochemical Separation Processes.- Computational Approaches for de novo Drug Design: Past, Present, and Future.- Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology.- Building and Interpreting Artificial Neural Network Models for Biological Systems.- A Novel Computational Approach for Biomarker Detection for Gene Expression based Computer Aided Diagnostic Systems for Breast Cancer.- Applying Machine Learning for Integration of Multi-modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues.- Leverage Large-scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.- Predicting Host Phenotype based on Gut Microbiome using a Convolutional Neural Network Approach.- Predicting Hot-Spots using a Deep Neural Network Approach.- Using Neural Networks for Relation Extraction from Biomedical Literature.- A Hybrid Levenberg-Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models.- Secure and Scalable Collection of Biomedical Data for Machine Learning Applications.- AI-based Methods and Technologies to Develop Wearable Devices for Prosthetics and Predictions of Degenerative Diseases.