Cartwright H. (ed.) Artificial Neural Networks

2nd edition. — Humana Press, 2015. — 353 p. — (Methods in Molecular Biology 1260).Artificial Neural Networks (ANNs) are among the most fundamental techniques within the field of Artificial Intelligence. Their operation loosely emulates the functioning of the human brain, but the value of an ANN extends well beyond its role as a biological model. An ANN can both memorize and reason: it provides a way in which a computer can learn from scratch about a previously unseen problem. Remarkably, the exact form of the problem is rarely critical; it might be financial (e.g., can we predict the direction of the stock market in the next few months?); it might be sociological (what factors make a face attractive?); it could be medical (can we tell from an X-ray whether a bone is broken?); or, as in this volume, the problem might be purely scientific.
This text brings together some productive and fascinating examples of how ANNs are applied in the biological sciences and related areas: from the analysis of intracellular sorting information to the prediction of the behavior of bacterial communities; from biometric authentication to studies of tuberculosis; from studies of gene signatures in breast cancer classification to the use of mass spectrometry in metabolite identification; from visual navigation to computer diagnosis of possible lesions; and more. The authors describe not only what they have done with ANNs but also how they have done it. Readers intrigued by the work described in this book will find numerous practical details, which should encourage further use of these rapidly developing tools.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 Multitasking Model for the Discovery of Potent and Safer Antibacterial Agents
Use of Artificial Neural Networks in the QSAR Prediction of Physicochemical 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: Toward 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 Behavior: 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