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Reverse Engineering of Regulatory Networks

Overview of attention for book
Cover of 'Reverse Engineering of Regulatory Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular Modeling Techniques and In-Silico Drug Discovery
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    Chapter 2 Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma
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    Chapter 3 Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures
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    Chapter 4 Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data
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    Chapter 5 Implementation of Exome Sequencing to Identify Rare Genetic Diseases
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    Chapter 6 Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures
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    Chapter 7 New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease
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    Chapter 8 A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network
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    Chapter 9 Big Data in Bioinformatics and Computational Biology: Basic Insights
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    Chapter 10 Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data
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    Chapter 11 Big Data Analysis in Computational Biology and Bioinformatics
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    Chapter 12 Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming.
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    Chapter 13 Hubs and Bottlenecks in Protein-Protein Interaction Networks
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    Chapter 14 Next-Generation Sequencing to Study the DNA Interaction.
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    Chapter 15 Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R
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    Chapter 16 Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data
  18. Altmetric Badge
    Chapter 17 Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology
Attention for Chapter 4: Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data
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Chapter title
Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data
Chapter number 4
Book title
Reverse Engineering of Regulatory Networks
Published in
Methods in molecular biology, January 2024
DOI 10.1007/978-1-0716-3461-5_4
Book ISBNs
978-1-07-163460-8, 978-1-07-163461-5
Authors

Chaturvedi, Aparna, Som, Anup

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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 29 December 2023.
All research outputs
#17,062,731
of 25,074,338 outputs
Outputs from Methods in molecular biology
#5,926
of 14,100 outputs
Outputs of similar age
#76,522
of 152,362 outputs
Outputs of similar age from Methods in molecular biology
#87
of 203 outputs
Altmetric has tracked 25,074,338 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,100 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 42nd percentile – i.e., 42% 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 152,362 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 203 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.

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