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Computational Methods for Predicting Post-Translational Modification Sites

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Cover of 'Computational Methods for Predicting Post-Translational Modification Sites'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling
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    Chapter 2 PLDMS: Phosphopeptide Library Dephosphorylation Followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences.
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    Chapter 3 FEPS: A Tool for Feature Extraction from Protein Sequence
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    Chapter 4 A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction
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    Chapter 5 iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features
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    Chapter 6 Functions of Glycosylation and Related Web Resources for Its Prediction
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    Chapter 7 Analysis of Posttranslational Modifications in Arabidopsis Proteins and Metabolic Pathways Using the FAT-PTM Database
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    Chapter 8 Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs
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    Chapter 9 Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins
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    Chapter 10 iPTMnet RESTful API for Post-translational Modification Network Analysis
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    Chapter 11 Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL
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    Chapter 12 Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models – Development, Validation, and Interpretation
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    Chapter 13 Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMapper
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    Chapter 14 PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins
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    Chapter 15 Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
Attention for Chapter 5: iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features
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Chapter title
iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features
Chapter number 5
Book title
Computational Methods for Predicting Post-Translational Modification Sites
Published in
Methods in molecular biology, January 2022
DOI 10.1007/978-1-0716-2317-6_5
Pubmed ID
Book ISBNs
978-1-07-162316-9, 978-1-07-162317-6
Authors

Dehzangi, Iman, Sharma, Alok, Shatabda, Swakkhar

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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 %
Unspecified 2 40%
Professor > Associate Professor 1 20%
Student > Doctoral Student 1 20%
Unknown 1 20%
Readers by discipline Count As %
Unspecified 2 40%
Biochemistry, Genetics and Molecular Biology 1 20%
Design 1 20%
Unknown 1 20%
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 13 June 2022.
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#20,865,839
of 23,485,953 outputs
Outputs from Methods in molecular biology
#9,852
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#20,382,870
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Outputs of similar age from Methods in molecular biology
#9,863
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So far Altmetric has tracked 12,978 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 12,955 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.