Reverse Engineering of Regulatory Networks

Author:   Sudip Mandal
Publisher:   Springer-Verlag New York Inc.
Edition:   1st ed. 2024
Volume:   2719
ISBN:  

9781071634608


Pages:   327
Publication Date:   08 October 2023
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Reverse Engineering of Regulatory Networks


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Author:   Sudip Mandal
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1st ed. 2024
Volume:   2719
Weight:   0.838kg
ISBN:  

9781071634608


ISBN 10:   1071634607
Pages:   327
Publication Date:   08 October 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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