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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

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    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
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    Chapter 3 Design and Analysis of Massively Parallel Reporter Assays Using FORECAST
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    Chapter 4 Modeling Protein Complexes and Molecular Assemblies Using Computational Methods
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    Chapter 5 From Genome Mining to Protein Engineering: A Structural Bioinformatics Route
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    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
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    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
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    Chapter 17 Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
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    Chapter 18 Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
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    Chapter 19 A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
Attention for Chapter 16: Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
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Chapter title
Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
Chapter number 16
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_16
Pubmed ID
Book ISBNs
978-1-07-162616-0, 978-1-07-162617-7
Authors

Doan, Le Minh Thao, Angione, Claudio, Occhipinti, Annalisa

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 12%
Student > Doctoral Student 2 8%
Student > Bachelor 2 8%
Student > Ph. D. Student 2 8%
Researcher 2 8%
Other 1 4%
Unknown 13 52%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 3 12%
Computer Science 3 12%
Medicine and Dentistry 2 8%
Biochemistry, Genetics and Molecular Biology 1 4%
Economics, Econometrics and Finance 1 4%
Other 1 4%
Unknown 14 56%
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 October 2022.
All research outputs
#20,028,856
of 24,614,554 outputs
Outputs from Methods in molecular biology
#8,658
of 13,846 outputs
Outputs of similar age
#335,243
of 459,223 outputs
Outputs of similar age from Methods in molecular biology
#518
of 718 outputs
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So far Altmetric has tracked 13,846 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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 459,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 718 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.