Artificial Intelligence in Biotechnology
eBook - PDF

Artificial Intelligence in Biotechnology

  1. 418 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Artificial Intelligence in Biotechnology

About this book

World has seen rapid development in the field of Information technology and Biotechnology over a decade. New experimental technologies developed in biotechnology and data available made it possible to perform experiments easily in less time and cost. These experiments also generate huge amount of data that may overwhelm even the most data?savvy researchers. Data generated during experimentation give lot of scope for companies that provide products and services in the field of biotechnology and new opportunities for researchers. This huge data may create challenges to the researches using low?throughput methods to handle and analyse data. Artificial intelligence plays prominent role in analysing huge data available in a systematic way and represent analysed data in a meaning full way. In todays time it is practically not possible to carry out research in biotechnology without utilising data available in public and private databases and artificial intelligence to analyse data. This book describes advancements and application of AI in the field of biotechnology.

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Yes, you can access Artificial Intelligence in Biotechnology by Preethi Kartan in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Biology. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Delve Publishing
Year
2020
eBook ISBN
9781774079881
Topic
Biological Sciences
Subtopic
Biology
Index
Biological Sciences

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. DECLARATION
  5. ABOUT THE EDITOR
  6. TABLE OF CONTENTS
  7. List of Contributors
  8. List of Abbreviations
  9. Preface
  10. Chapter 1 AI, Big Data, and Robots for the Evolution of Biotechnology
  11. Chapter 2 Artificial Intelligence Applications in Biomedicine
  12. Chapter 3 Artificial Intelligence and Machine Learning in Clinical Development: A Translational Perspective
  13. Chapter 4 Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Autoencoder
  14. Chapter 5 FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification
  15. Chapter 6 A Pretraining-Retraining Strategy of Deep Learning Improves Cell-Specific Enhancer Predictions
  16. Chapter 7 HiCeekR: A Novel Shiny App for Hi-C Data Analysis
  17. Chapter 8 ContraDRG: Automatic Partial Charge Prediction by Machine Learning
  18. Chapter 9 Measurement of Conditional Relatedness between Genes Using Fully Convolutional Neural Network
  19. Chapter 10 Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease
  20. Chapter 11 Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data
  21. Chapter 12 Deep Learning for Super-Resolution in a Field Emission Scanning Electron Microscope
  22. Chapter 13 Artificial Intelligence for Aging and Longevity Research: Recent Advances and Perspectives
  23. Chapter 14 Artificial Intelligence in the Lab: Ask not What Your Computer can do for you
  24. Chapter 15 Synthetic Biology Routes to Bio-artificial Intelligence
  25. Chapter 16 Better Medicine through Machine Learning: What’s real, and what’s Artificial?
  26. Chapter 17 Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.
  27. Chapter 18 Artificial Intelligence in Medical Applications
  28. Index
  29. Back Cover