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Reverse Engineering of Regulatory Networks [Pehme köide]

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  • Formaat: Paperback / softback, 327 pages, kõrgus x laius: 254x178 mm, 64 Illustrations, color; 8 Illustrations, black and white; X, 327 p. 72 illus., 64 illus. in color., 1 Paperback / softback
  • Sari: Methods in Molecular Biology 2719
  • Ilmumisaeg: 08-Oct-2024
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071634631
  • ISBN-13: 9781071634639
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  • Pehme köide
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Reverse Engineering of Regulatory NetworksSuurem pilt
  • Formaat: Paperback / softback, 327 pages, kõrgus x laius: 254x178 mm, 64 Illustrations, color; 8 Illustrations, black and white; X, 327 p. 72 illus., 64 illus. in color., 1 Paperback / softback
  • Sari: Methods in Molecular Biology 2719
  • Ilmumisaeg: 08-Oct-2024
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071634631
  • ISBN-13: 9781071634639
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781071634639.html
Märksõnad:

This volume details the development of updated dry lab and wet lab based methods for the reconstruction of Gene regulatory networks (GRN). 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. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Reverse Engineering of Regulatory Networks aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge. 

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.