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Artificial Neural Networks

Overview of attention for book
Cover of 'Artificial Neural Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to the analysis of the intracellular sorting information in protein sequences: from molecular biology to artificial neural networks.
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    Chapter 2 Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N
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    Chapter 3 Predicting bacterial community assemblages using an artificial neural network approach.
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    Chapter 4 A General ANN-Based Multitasking Model for the Discovery of Potent and Safer Antibacterial Agents
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    Chapter 5 Use of Artificial Neural Networks in the QSAR Prediction of Physicochemical Properties and Toxicities for REACH Legislation
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    Chapter 6 Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry
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    Chapter 7 Prediction of Bioactive Peptides Using Artificial Neural Networks
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    Chapter 8 AutoWeka: Toward an Automated Data Mining Software for QSAR and QSPR Studies
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    Chapter 9 Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)
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    Chapter 10 GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction
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    Chapter 11 Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control
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    Chapter 12 Application of Artificial Neural Networks in Computer-Aided Diagnosis
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    Chapter 13 Developing a Multimodal Biometric Authentication System Using Soft Computing Methods
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    Chapter 14 Using Neural Networks to Understand the Information That Guides Behavior: A Case Study in Visual Navigation
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    Chapter 15 Jump neural network for real-time prediction of glucose concentration.
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    Chapter 16 Preparation of Ta-O-Based Tunnel Junctions to Obtain Artificial Synapses Based on Memristive Switching
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    Chapter 17 Architecture and Biological Applications of Artificial Neural Networks: A Tuberculosis Perspective
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    Chapter 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
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    Chapter 19 QSAR/QSPR as an Application of Artificial Neural Networks
Attention for Chapter 2: Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N
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Chapter title
Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N
Chapter number 2
Book title
Artificial Neural Networks
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2239-0_2
Pubmed ID
25502373
Book ISBNs
978-1-4939-2238-3, 978-1-4939-2239-0
Authors

Yang Shen, Ad Bax

Abstract

Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors, and the artificial neural network based TALOS-N program has been trained to extract backbone and side-chain torsion angles from (1)H, (15)N, and (13)C shifts. The program is quite robust and typically yields backbone torsion angles for more than 90 % of the residues and side-chain χ 1 rotamer information for about half of these, in addition to reliably predicting secondary structure. The use of TALOS-N is illustrated for the protein DinI, and torsion angles obtained by TALOS-N analysis from the measured chemical shifts of its backbone and (13)C(β) nuclei are compared to those seen in a prior, experimentally determined structure. The program is also particularly useful for generating torsion angle restraints, which then can be used during standard NMR protein structure calculations.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 26%
Researcher 20 22%
Student > Bachelor 6 7%
Student > Doctoral Student 3 3%
Other 3 3%
Other 11 12%
Unknown 24 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 27%
Chemistry 14 16%
Agricultural and Biological Sciences 9 10%
Computer Science 3 3%
Engineering 3 3%
Other 7 8%
Unknown 30 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 January 2015.
All research outputs
#18,387,239
of 22,775,504 outputs
Outputs from Methods in molecular biology
#7,870
of 13,091 outputs
Outputs of similar age
#255,683
of 352,932 outputs
Outputs of similar age from Methods in molecular biology
#479
of 996 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,091 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 352,932 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 996 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.

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