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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 14: Next-Generation Sequencing to Study the DNA Interaction.
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Chapter title
Next-Generation Sequencing to Study the DNA Interaction.
Chapter number 14
Book title
Reverse Engineering of Regulatory Networks
Published in
Methods in molecular biology, January 2024
DOI 10.1007/978-1-0716-3461-5_14
Pubmed ID
Book ISBNs
978-1-07-163460-8, 978-1-07-163461-5
Authors

Kathiresan, Nachammai, Ramachandran, Srinithi, Kulanthaivel, Langeswaran

Abstract

Next-generation sequencing (NGS) has transformed genomics by allowing researchers to sequence DNA and RNA at highest speed, accuracy, and cost-effectiveness. Researchers investigate DNA interactions with the help next-generation sequencing with a great deal of information. Over the last decade, NGS technologies have advanced significantly, with the development of several platforms, including Illumina, PacBio, and Oxford Nanopore, each offering distinct advantages and uses. The use of next-generation sequencing (NGS) has aided in the discovery of genetic variations, gene expression patterns, and epigenetic modifications connected with a variety of diseases, including cancer, neurological disorders, and infectious diseases. By identifying these regions, we can control the expression of genes, cellular signaling pathways, and other key biological processes. NGS is an effective method for researching DNA interactions that has completely transformed the area of genomics. NGS has also played an important part in personalized medicine, enabling the discovery of disease-causing mutations and the creation of targeted medicines. Finally, NGS has transformed the field of genomics, resulting in new discoveries and applications in medicine, environmental sciences, and other fields.

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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 08 October 2023.
All research outputs
#15,510,539
of 24,580,204 outputs
Outputs from Methods in molecular biology
#4,553
of 13,820 outputs
Outputs of similar age
#3,949
of 8,824 outputs
Outputs of similar age from Methods in molecular biology
#2
of 20 outputs
Altmetric has tracked 24,580,204 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,820 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 63% 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 8,824 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 52% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.