Advances in bioengineering
This book provides a single source of information on three major bioengineering areas: engineering at the cellular and molecular level; biomedical devices / instrument engineering; and data engineering. It explores the latest strategies that are essential to advancing our understanding of the mechan...
| Other Authors: | Vyas, Renu,, SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
[2020]
|
| Physical Description: |
1 online resource (xiii, 210 pages) : illustrations. |
| Subjects: |
| Summary: |
This book provides a single source of information on three major bioengineering areas: engineering at the cellular and molecular level; biomedical devices / instrument engineering; and data engineering. It explores the latest strategies that are essential to advancing our understanding of the mechanisms of human diseases, the development of new enzyme-based technologies, diagnostics, prosthetics, high-performance computing platforms for managing huge amounts of biological data, and the use of deep learning methods to create predictive models. The book also highlights the growing importance of integrating chemistry into life sciences research, most notably concerning the development and evaluation of nanomaterials and nanoparticles and their interactions with biological material. The underlying interdisciplinary theme of bioengineering is addressed in a range of multifaceted applications and worked out examples provided in each chapter. |
|---|---|
| Item Description: |
Includes bibliographical references. This book provides a single source of information on three major bioengineering areas: engineering at the cellular and molecular level; biomedical devices / instrument engineering; and data engineering. It explores the latest strategies that are essential to advancing our understanding of the mechanisms of human diseases, the development of new enzyme-based technologies, diagnostics, prosthetics, high-performance computing platforms for managing huge amounts of biological data, and the use of deep learning methods to create predictive models. The book also highlights the growing importance of integrating chemistry into life sciences research, most notably concerning the development and evaluation of nanomaterials and nanoparticles and their interactions with biological material. The underlying interdisciplinary theme of bioengineering is addressed in a range of multifaceted applications and worked out examples provided in each chapter. About the Editor -- Part I: Data Engineering -- 1: Modelling of Protein Complexes Involved in Signalling Pathway for Non-small Cell Lung Cancer -- 1.1 Introduction to Pathway Modelling -- 1.2 Methods in Pathway Modelling -- 1.2.1 Mathematical Modelling Approach -- 1.2.1.1 Boolean Networking -- 1.2.1.2 Ordinary Differential Equation -- 1.2.1.3 Stoichiometric Approach -- 1.2.2 Network-Based Modelling Approach -- 1.2.2.1 Bayesian Method -- 1.2.2.2 Gaussian Networking -- 1.2.2.3 Maximum Likelihood Approach -- 1.2.2.4 Hidden Markov Model -- 1.2.2.5 Latent Variable Model -- 1.2.3 Molecular Modelling Approach in Lung Cancer. 1.3 Signalling Pathways in NSCLC -- 1.3.1 MAPK Pathway -- 1.3.2 NF-{u0199}B Pathway -- 1.3.3 RAS Pathway -- 1.4 Molecular Dynamics Approach in NSCLC Pathway -- 1.5 Conclusion -- References -- 2: Role of BioJava in the Department of Bioinformatics Tools -- 2.1 What Is Bioinformatics? -- 2.2 Application of Java in Bioinformatics -- 2.3 Introduction to BioJava -- 2.4 The BioJava Modules -- 2.4.1 Core of BioJava -- 2.4.2 Alignment Module -- 2.4.3 Structure Module -- 2.4.4 ModFinder Module -- 2.4.5 Protein Disorder Module -- 2.4.6 Web Service Access Module -- 2.5 The BioJava Packages -- 2.5.1 Sequence Matching. 2.5.2 Symbolic Representation for Sequence -- 2.5.3 Biological Sequence Data -- 2.5.4 Process and Produce Flat File of Sequences -- 2.5.5 GUI Representation of the Sequences -- 2.5.6 Sequence Database -- 2.5.7 Input Output Utility -- 2.5.8 Network Programming Utility -- 2.5.9 To Manage and Generate XML Document -- 2.5.10 To Generate HTML Reports from Blast Output -- 2.6 BioJava: A Tutorial with the NetBeans IDE -- 2.6.1 Download and Install jdk 1.8+ Versions -- 2.6.2 Download BioJava packages -- 2.6.3 Add .jar File in NetBeans Project -- 2.7 Design and Implementation -- 2.8 Exception Handling in BioJava. 2.9 How to Contribute in BioJava Open-Source Project? -- 2.10 Conclusions -- References -- 3: Overview of Machine Learning Methods in ADHD Prediction -- 3.1 Attention Deficit Hyperactivity Disorder (ADHD) -- 3.1.1 Symptoms and Causes of ADHD -- 3.1.2 Diagnosis and Prediction of ADHD -- 3.2 Overview of Various Machine Learning Methods in Predictive Analysis -- 3.2.1 ADHD Prediction Using Machine Learning Models -- 3.3 Genetic Programming -- 3.4 Conclusion -- References -- 4: Applications of Deep Learning in Drug Discovery -- 4.1 Introduction -- 4.2 Machine Learning Primer -- 4.3 ANNs and Deep Learning. |
| Physical Description: |
1 online resource (xiii, 210 pages) : illustrations. |
| Bibliography: |
Includes bibliographical references. |
| ISBN: |
9789811520631 9811520631 9789811520648 981152064X 9789811520655 9811520658 |