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Artificial Neural Networks de Hugh Cartwright

Artificial Neural Networks: Methods in Molecular Biology, cartea 1260

Editat de Hugh Cartwright
en Limba Engleză Paperback – 30 apr 2017
This volume presents examples of how ANNs are applied in biological sciences and related areas. Chapters focus on the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication, studies of Tuberculosis, gene signatures in breast cancer classification, 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: Second Edition aids scientists in continuing to study Artificial Neural Networks (ANNs).
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Specificații

ISBN-13: 9781493948932
ISBN-10: 1493948938
Pagini: 340
Ilustrații: XI, 340 p. 92 illus., 65 illus. in color.
Dimensiuni: 178 x 254 x 24 mm
Ediția:Softcover reprint of the original 2nd ed. 2015
Editura: Springer
Colecția Springer
Seria Methods in Molecular Biology

Locul publicării:New York, NY, United States

Cuprins

Introduction To The Analysis Of The Intracellular Sorting Information In Protein Sequences: From Molecular Biology To Artificial Neural Networks.- Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N.- Predicting Bacterial Community Assemblages using an Artificial Neural Network Approach.- A General ANN-Based Multi-Tasking Model For The Discovery Of Potent and Safer Antibacterial Agents.- Use of Artificial Neural Networks in the QSAR Prediction of Physico-chemical Properties and Toxicities for REACH Legislation.- Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry.- Prediction of Bioactive Peptides using Artificial Neural Networks.- AutoWeka: Towards an Automated Data Mining Software for QSAR and QSPR Studies.- Ligand Biological Activity Predictions Using Fingerprint-based Artificial Neural Networks (FANN-QSAR).- GENN: A General Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction.- Modulation of Grasping Force in Prosthetic Hands Using Neural Network-based Predictive Control.- Application of Artificial Neural Networks in Computer-aided Diagnosis.- Developing A Multimodal Biometric Authentication System Using Soft Computing Methods.- Using Neural Networks To Understand The Information That Guides Behaviour: A Case Study In Visual Navigation.- Jump Neural Network For Real-Time Prediction Of Glucose Concentration.- Preparation of Ta-O-based Tunnel Junctions To Obtain Artificial Synapses Based On Memristive Switching.- Architecture and Biological Applications of Artificial Neural Networks: a Tuberculosis Perspective.- Neural Networks And Fuzzy Clustering Methods For Assessing The Efficacy Of Microarray Based Intrinsic Gene Signatures In Breast Cancer Classification And The Character And Relations Of Identified Subtypes.- QSAR/QSPR as an Application of Artificial Neural Networks.

Textul de pe ultima copertă

This volume presents examples of how ANNs are applied in biological sciences and related areas. Chapters focus on the analysis of intracellular sorting information,  prediction of the behavior of bacterial communities,  biometric authentication,  studies of Tuberculosis,  gene signatures in breast cancer classification,  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: Second Edition aids scientists in continuing to study Artificial Neural Networks (ANNs).

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 Includes supplementary material: sn.pub/extras