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Computational Methods in Systems Biology

Overview of attention for book
Cover of 'Computational Methods in Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 (Mathematical) Logic for Systems Biology (Invited Paper)
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    Chapter 2 Generalized Method of Moments for Stochastic Reaction Networks in Equilibrium
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    Chapter 3 Inference of Delayed Biological Regulatory Networks from Time Series Data
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    Chapter 4 Matching Models Across Abstraction Levels with Gaussian Processes
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    Chapter 5 Target Controllability of Linear Networks
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    Chapter 6 High-Performance Symbolic Parameter Synthesis of Biological Models: A Case Study
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    Chapter 7 Influence Systems vs Reaction Systems
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    Chapter 8 Local Traces: An Over-Approximation of the Behaviour of the Proteins in Rule-Based Models
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    Chapter 9 Bifurcation Analysis of Cardiac Alternans Using \(\delta \) -Decidability
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    Chapter 10 A Stochastic Hybrid Approximation for Chemical Kinetics Based on the Linear Noise Approximation
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    Chapter 11 Autonomous and Adaptive Control of Populations of Bacteria Through Environment Regulation
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    Chapter 12 Parameter Estimation for Reaction Rate Equation Constrained Mixture Models
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    Chapter 13 Normalizing Chemical Reaction Networks by Confluent Structural Simplification
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    Chapter 14 Fast Simulation of Probabilistic Boolean Networks
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    Chapter 15 Formal Quantitative Analysis of Reaction Networks Using Chemical Organisation Theory
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    Chapter 16 Goal-Oriented Reduction of Automata Networks
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    Chapter 17 Hybrid Reductions of Computational Models of Ion Channels Coupled to Cellular Biochemistry
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    Chapter 18 Formal Modeling and Analysis of Pancreatic Cancer Microenvironment
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    Chapter 19 ASSA-PBN 2.0: A Software Tool for Probabilistic Boolean Networks
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    Chapter 20 E-Cyanobacterium.org: A Web-Based Platform for Systems Biology of Cyanobacteria
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    Chapter 21 PREMER: Parallel Reverse Engineering of Biological Networks with Information Theory
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

1 news outlet
8 X users


6 Dimensions

Readers on

5 Mendeley
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Computational Methods in Systems Biology
Published by
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-45177-0
978-3-31-945176-3, 978-3-31-945177-0

Ezio Bartocci, Pietro Lio, Nicola Paoletti

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
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 %
Professor > Associate Professor 1 20%
Student > Bachelor 1 20%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Computer Science 1 20%
Agricultural and Biological Sciences 1 20%
Psychology 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 January 2017.
All research outputs
of 22,886,568 outputs
Outputs from Lecture notes in computer science
of 8,127 outputs
Outputs of similar age
of 393,716 outputs
Outputs of similar age from Lecture notes in computer science
of 581 outputs
Altmetric has tracked 22,886,568 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 95% 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 393,716 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 581 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.