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Artificial Neural Networks: Methods in Molecular Biology, cartea 2190

Editat de Hugh Cartwright
en Limba Engleză Paperback – 18 aug 2021
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.

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Specificații

ISBN-13: 9781071608289
ISBN-10: 1071608282
Pagini: 359
Ilustrații: XII, 359 p. 134 illus., 114 illus. in color.
Dimensiuni: 178 x 254 mm
Greutate: 0.64 kg
Ediția:3rd ed. 2021
Editura: Springer Us
Colecția Humana
Seria Methods in Molecular Biology

Locul publicării:New York, NY, United States

Cuprins

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.  

Textul de pe ultima copertă

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.

Caracteristici

Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key notes and implementation advice from the experts?