Artificial Intelligence in Biotechnology

Preethi Kartan

• Éditeur: Delve Publishing
• Année: 2020
• ISBN:
  9781774079881
• Sujet: Biological Sciences
• Sous-sujet: Biology

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.

Table des matières

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