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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
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    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
  13. Altmetric Badge
    Chapter 12 Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology
  14. Altmetric Badge
    Chapter 13 Overview of Bioinformatics Software and Databases for Metabolic Engineering
  15. Altmetric Badge
    Chapter 14 Computational Simulation of Tumor-Induced Angiogenesis
  16. Altmetric Badge
    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 18: Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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6 X users

Citations

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5 Dimensions

Readers on

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14 Mendeley
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Chapter title
Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
Chapter number 18
Book title
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology
Published in
Methods in molecular biology, October 2022
DOI 10.1007/978-1-0716-2617-7_18
Pubmed ID
Book ISBNs
978-1-07-162616-0, 978-1-07-162617-7
Authors

Miroslava Cuperlovic-Culf, Thao Nguyen-Tran, Steffany A. L. Bennett, Cuperlovic-Culf, Miroslava, Nguyen-Tran, Thao, Bennett, Steffany A. L.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 14%
Student > Master 2 14%
Professor > Associate Professor 1 7%
Other 1 7%
Unspecified 1 7%
Other 0 0%
Unknown 7 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 14%
Biochemistry, Genetics and Molecular Biology 2 14%
Arts and Humanities 1 7%
Unspecified 1 7%
Unknown 8 57%
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 19 October 2022.
All research outputs
#13,329,938
of 23,885,338 outputs
Outputs from Methods in molecular biology
#3,316
of 13,512 outputs
Outputs of similar age
#156,728
of 427,331 outputs
Outputs of similar age from Methods in molecular biology
#48
of 214 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,512 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 75% 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 427,331 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 63% of its contemporaries.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.