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Computational Methods for Predicting Post-Translational Modification Sites

Overview of attention for book
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 15: Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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4 X users

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Chapter title
Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
Chapter number 15
Book title
Computational Methods for Predicting Post-Translational Modification Sites
Published in
Methods in molecular biology, June 2022
DOI 10.1007/978-1-0716-2317-6_15
Pubmed ID
Book ISBNs
978-1-07-162316-9, 978-1-07-162317-6
Authors

Pakhrin, Subash C., Pokharel, Suresh, Saigo, Hiroto, KC, Dukka B.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 33%
Unspecified 1 17%
Researcher 1 17%
Professor > Associate Professor 1 17%
Unknown 1 17%
Readers by discipline Count As %
Unspecified 1 17%
Biochemistry, Genetics and Molecular Biology 1 17%
Computer Science 1 17%
Immunology and Microbiology 1 17%
Engineering 1 17%
Other 0 0%
Unknown 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 June 2022.
All research outputs
#13,182,656
of 22,769,322 outputs
Outputs from Methods in molecular biology
#3,450
of 13,090 outputs
Outputs of similar age
#173,405
of 438,306 outputs
Outputs of similar age from Methods in molecular biology
#90
of 559 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,090 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 72% of its peers.
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 438,306 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 559 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.