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Protein Function Prediction for Omics Era

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Cover of 'Protein Function Prediction for Omics Era'

Table of Contents

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    Book Overview
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    Chapter 1 Computational Protein Function Prediction: Framework and Challenges
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    Chapter 2 Enhanced Sequence-Based Function Prediction Methods and Application to Functional Similarity Networks
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    Chapter 3 Gene Cluster Prediction and Its Application to Genome Annotation
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    Chapter 4 Functional Inference in Microbial Genomics Based on Large-Scale Comparative Analysis
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    Chapter 5 Predicting Protein Functional Sites with Phylogenetic Motifs: Past, Present and Beyond
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    Chapter 6 Exploiting Protein Structures to Predict Protein Functions
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    Chapter 7 Sequence Order Independent Comparison of Protein Global Backbone Structures and Local Binding Surfaces for Evolutionary and Functional Inference
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    Chapter 8 Protein Binding Ligand Prediction Using Moments-Based Methods
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    Chapter 9 Computational Methods for Predicting DNA-Binding Sites at a Genomic Scale
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    Chapter 10 Electrostatic Properties for Protein Functional Site Prediction
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    Chapter 11 Function Prediction of Genes: From Molecular Function to Cellular Function
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    Chapter 12 Predicting Gene Function Using Omics Data: From Data Preparation to Data Integration
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    Chapter 13 Protein Function Prediction Using Protein–Protein Interaction Networks
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    Chapter 14 KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource
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    Chapter 15 Towards Elucidation of the Escherichia coli K-12 Unknowneome
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Chapter title
Protein Function Prediction Using Protein–Protein Interaction Networks
Chapter number 13
Book title
Protein Function Prediction for Omics Era
Published by
Springer, Dordrecht, January 2011
DOI 10.1007/978-94-007-0881-5_13
Book ISBNs
978-9-40-070880-8, 978-9-40-070881-5
Authors

Hon Nian Chua, Guimei Liu, Limsoon Wong

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 60%
Student > Ph. D. Student 2 40%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 60%
Computer Science 2 40%