Reverse Engineering of Regulatory Networks

Inbunden, Engelska, 2023

3 159 kr

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Chapters guide readers through culprit genes, in-silico drug discovery techniques, genome-wide ChIP-X data, high-Throughput Transcriptomic Data Exome Sequencing, Next-Generation Sequencing, Fuorescence Spectroscopy, data analysis in Bioinformatics, Computational Biology, and S-system based modeling of GRN.

Produktinformation

Utgivningsdatum2023-10-08
Mått178 x 254 x 24 mm
Vikt838 g
FormatInbunden
SpråkEngelska
SerieMethods in Molecular Biology
Antal sidor327
FörlagSpringer-Verlag New York Inc.
ISBN9781071634608

Tillhör följande kategorier

Biomedicinsk teknik inom Medicin
Biovetenskap inom Naturvetenskap och teknik

Molecular Modeling Techniques and in-Silico Drug Discovery.- Systems Biology Approach to Analyse Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous cell Carcinoma.- Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures.- Inference of Dynamic Growth Regulatory Network in Cancer Using high-Throughput Transcriptomic Data.- Implementation of Exome Sequencing to Identify Rare Genetic Diseases.- Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures.- New Insights into Clinical Management for Sickle-Cell Disease: Uncovering the Significance Pathways Affected By the Involvement of Sickle Cell Disease.- A Review on Computational Approach for S-system Based Modeling of Gene Regulatory Network.- Big Data in Bioinformatics and Computational Biology: Basic Insights.- Identification of Culprit Genes for Different Diseases by Analysing Microarray Data.- Big Data Analysis in Computational Biology and Bioinformatics.- Prediction and Analysis of Transcription Factor Binding Sites to Understand Gene Regulation: Practical Examples and Case Studies using R Programming.- Hubs and Bottlenecks in Protein-Protein Interaction Networks.- Next-Generation Sequencing to Study the DNA Interaction Nac Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.- Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.- Computational inference of Gene Regulatory Network using genome-wide ChIP-X data.- Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology.