Description
Artificial Neural Networks (2nd Ed., 2nd ed. 2015)
Methods in Molecular Biology Series, Vol. 1260
Language: EnglishSubject for Artificial Neural Networks:
Publication date: 04-2017
Support: Print on demand
340 p. · 17.8x25.4 cm · Hardback
Description
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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).
1. Introduction To The Analysis Of The Intracellular Sorting Information In Protein Sequences: From Molecular Biology To Artificial Neural Networks
R. Claudio Aguilar
2. Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N
Yang Shen and Ad Bax
3. Predicting Bacterial Community Assemblages using an Artificial Neural Network Approach
Peter Larsen, Yang Dai and Frank R. Collart
4. A General ANN-Based Multi-Tasking Model For The Discovery Of Potent and Safer Antibacterial Agents
A. Speck-Planche and M. N. D. S. Cordeiro
5. Use of Artificial Neural Networks in the QSAR Prediction of Physico-chemical Properties and Toxicities for REACH Legislation
John C. Dearden and Philip H. Rowe
6. Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry
J. H. Miller, B. T. Schrom, and L. J. Kangas
7. Prediction of Bioactive Peptides using Artificial Neural Networks
David Andreu and Marc Torrent
8. AutoWeka: Towards an Automated Data Mining Software for QSAR and QSPR Studies
Chanin Nantasenamat, Apilak Worachartcheewan, Saksiri Jamsak, Likit Preeyanon, Watshara Shoombuatong, Saw Simeon, Prasit Mandi, Chartchalerm Isarankura-Na-Ayudhyaand Virapong Prachayasittikul
9. Ligand Biological Activity Predictions Using Fingerprint-based Artificial Neural Networks (FANN-QSAR)
Kyaw Z. Myint and Xiang-Qun Xie
10. GENN: A General Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction
Eshel Faraggi and Andrzej Kloczkowski
11. Modulation of Grasping Force in Prosthetic Hands Using Neural Network-based Predictive Control
Cristian F. Pasluosta and Alan W.L. Chiu
12. Application of Artificial Neural Networks in Computer-aided Diagnosis
Bei Liu
13. Developing A Multimodal Biometric Authentication System Using Soft Computing Methods
Mario Malcangi
14. Using Neural Networks To Understand The Information That Guides Behaviour: A Case Study In Visual Navigation
Andrew Philippides, Paul Graham, Bart Baddeley and Philip Husbands
15. Jump Neural Network For Real-Time Prediction Of Glucose Concentration
Chiara Zecchin, Andrea Facchinetti, Giovanni Sparacino and Claudio Cobelli
16. Preparation of Ta-O-based Tunnel Junctions To Obtain Artificial Synapses Based On Memristive Switching
Stefan Niehörster and Andy Thomas
17. Architecture and Biological Applications of Artificial Neural Networks: a Tuberculosis Perspective
Jerry A Darsey
18. 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
Sandhya Samarasinghe and Amphun Chaiboonchoe
19. QSAR/QSPR as an Application of Artificial Neural Networks
Narelle Montañez-Godínez, Aracely C. Martínez-Olguín, Omar Deeb, Ramón Garduño-Juárez, Guillermo Ramírez-Galicia
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