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Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

Overview of attention for book
Cover of 'Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications
  3. Altmetric Badge
    Chapter 2 Synthetic Biology Meets Machine Learning
  4. Altmetric Badge
    Chapter 3 Design and Analysis of Massively Parallel Reporter Assays Using FORECAST
  5. Altmetric Badge
    Chapter 4 Modeling Protein Complexes and Molecular Assemblies Using Computational Methods
  6. Altmetric Badge
    Chapter 5 From Genome Mining to Protein Engineering: A Structural Bioinformatics Route
  7. Altmetric Badge
    Chapter 6 Creating De Novo Overlapped Genes
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    Chapter 7 Design of Gene Boolean Gates and Circuits with Convergent Promoters
  9. Altmetric Badge
    Chapter 8 Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications
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    Chapter 9 Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization
  11. Altmetric Badge
    Chapter 10 Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology
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    Chapter 11 Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio natriegens for the Production of 1,3-Propanediol
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    Chapter 12 Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology
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    Chapter 13 Overview of Bioinformatics Software and Databases for Metabolic Engineering
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    Chapter 14 Computational Simulation of Tumor-Induced Angiogenesis
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    Chapter 15 Computational Methods and Deep Learning for Elucidating Protein Interaction Networks
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    Chapter 16 Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
  18. Altmetric Badge
    Chapter 17 Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
  19. Altmetric Badge
    Chapter 18 Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
  20. Altmetric Badge
    Chapter 19 A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
Attention for Chapter 17: Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
4 X users

Citations

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5 Dimensions

Readers on

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7 Mendeley
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Chapter title
Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
Chapter number 17
Book title
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology
Published in
Methods in molecular biology, January 2023
DOI 10.1007/978-1-0716-2617-7_17
Pubmed ID
Book ISBNs
978-1-07-162616-0, 978-1-07-162617-7
Authors

Bonetta Valentino, Rosalin, Ebejer, Jean-Paul, Valentino, Gianluca

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X Demographics

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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 14%
Unknown 6 86%
Readers by discipline Count As %
Energy 1 14%
Unknown 6 86%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 October 2022.
All research outputs
#13,871,219
of 23,523,017 outputs
Outputs from Methods in molecular biology
#3,786
of 13,389 outputs
Outputs of similar age
#171,729
of 435,739 outputs
Outputs of similar age from Methods in molecular biology
#82
of 501 outputs
Altmetric has tracked 23,523,017 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,389 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 69% 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 435,739 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 58% of its contemporaries.
We're also able to compare this research output to 501 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.