Smart biolabs of the future / Sascha Beutel, Felix Lenk, editors ; with contributions by M. Arnold [and more].
This book reviews the advances in data gathering and processing in the biotech laboratory environment, and it sheds new lights on the various aspects that are necessary for the implementation of intelligent laboratory architecture and infrastructure. Smart technologies are increasingly dominating ou...
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| Other Authors: | , |
|---|---|
| Format: | Ebook |
| Language: | English |
| Published: |
Cham :
Springer,
2022.
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| Series: | Advances in biochemical engineering/biotechnology ;
182. |
| Subjects: | |
| Online Access: | Springer eBooks |
Table of Contents:
- Intro
- Preface
- Contents
- Part I: Data Gathering Part
- Automation for Life Science Laboratories
- 1 Introduction
- 1.1 Definition Automation
- 1.2 Automation in Life Sciences
- 2 Robots in Life Sciences
- 2.1 Introduction
- 2.2 Stationary Robots
- 2.3 Mobile Robots
- 3 Innovative Automation Devices
- 3.1 Introduction
- 3.2 Devices for Labware Handling
- 3.3 Devices for Sample Processing
- 4 Automation Strategies
- 5 Summary and Outlook
- References
- Evolution of Artificial Intelligence-Powered Technologies in Biomedical Research and Healthcare
- 1 Prologue
- 2 Introduction
- 2.1 Brief Historical Overview of AI
- 2.2 Understanding AI
- 2.3 AI Inception
- 2.4 AI Training Workflow
- 3 Machine Learning
- 3.1 Supervised Learning
- 3.2 Semi-Supervised Learning
- 3.3 Self-Supervised Learning
- 3.4 A Note on Reinforcement Learning
- 4 Deep Learning
- 4.1 Artificial Neural Networks (ANN)
- 4.2 Brief Overview of DL Models
- 4.2.1 Feed Forward Neural Networks (FFNNs)
- 4.2.2 Recurrent Neural Networks (RNN)
- 4.2.3 Convolutional Neural Network (CNN)
- 4.2.4 Autoencoders (AEs)
- 5 AI-Derived Applications in Biology and Biomedical Research
- 5.1 AI in Structural Biology
- 5.2 AI in Clinical Genomics
- 5.3 AI in Image Analysis
- 5.4 AI in Multiomics Analysis
- 5.5 AI in Documentation and Data Management
- 5.6 Deep Learning in Drug Discovery
- 5.6.1 Phenotypic Drug Discovery
- 5.6.2 Virtual Screening
- 5.6.3 Target-Based Drug Discovery
- 6 AI as a Powerful Tool for Precision Medicine
- 7 Limitations of AI
- 8 Conclusions
- References
- A Comprehensive IT Infrastructure for an Enzymatic Product Development in a Digitalized Biotechnological Laboratory
- 1 Introduction
- 2 Solutions for Laboratory Digitalization and Digitization
- 2.1 Process Overview
- 2.2 Software in the Laboratory Infrastructure
- 2.2.1 Electronic Lab Notebook
- 2.2.2 Laboratory Information and Management System
- 2.2.3 Laboratory Execution Software
- 2.2.4 Manufacturing and Execution System
- 2.2.5 Supervisory Control and Data Acquisition and Distributed Control System
- 2.2.6 Overview Software
- 2.3 Data Exchange Between Laboratory Software
- 2.4 Device Integration
- 3 Discussion
- 4 Conclusion and Outlook
- References
- Human-Device Interaction in the Life Science Laboratory
- 1 Introduction
- 2 The Evolution of Laboratory Devices
- 3 The Rise and Evolution of Software in the Lab
- 4 Natural User Interfaces in the Laboratory
- 4.1 Touch-Based User Interfaces
- 4.1.1 Smartphone and Tablet Applications in the (Bio)chemical Laboratory
- 4.1.2 Assistance Applications for Smartphones and Tablets in the Laboratory
- 4.1.3 Smartphones and Tablets as Equipment Replacement
- 4.2 Touchless User Interfaces
- 4.2.1 Gesture and Motion-Based Interfaces in the Laboratory
- 4.2.2 Voice User Interfaces in the Laboratory
- 4.2.3 AI in Natural Language Processing.