Artificial Intelligence Technologies for Computational Biology

✍ Scribed by Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 345
• Category: Library

✦ Synopsis

This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing.

It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems.

The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology.

This book:

• Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis.

• Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems.

• Presents the application of evolutionary computations for fractal visualization of sequence data.

• Explores the use of genetic algorithms for pair-wise and multiple sequence alignments.

• Examines the roles of efficient computational techniques in biology.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Contributors
List of Figures
List of Tables
Chapter 1: Graph Representation Learning for Protein Classification
Chapter 2: Extraction of Sequence-Based Features for Prediction of Methylation Sites in Protein Sequences
Chapter 3: A Taxonomy of e-Healthcare Techniques and Solutions: Challenges and Future Directions
Chapter 4: Classification of Lung Diseases Using Machine Learning Techniques
Chapter 5: Multi Objective Bacterial Foraging Optimization: A Survey
Chapter 6: Artificial Intelligence for Biomedical Informatics
Chapter 7: A Novel Approach for Feature Selection Using Artificial Neural Networks and Particle Swarm Optimization
Chapter 8: In Search for the Optimal Preprocessing Technique for Deep Learning-Based Diabetic Retinopathy Stage Classification from Retinal Fundus Images
Chapter 9: Cancer Diagnosis from Histopathology Images Using Deep Learning: A Review
Chapter 10: Skin Lesion Classification by Using Deep Tree-CNN
Chapter 11: Hybrid Deep Learning Model to Diagnose Covid-19 on its Early Stages Using Lung CT Images
Chapter 12: Impact of Machine Learning Practices on Biomedical Informatics, Its Challenges and Future Benefits
Chapter 13: Recognition of Types of Arrhythmia: An Implementation of Ensembling Techniques Using ECG Beat
Chapter 14: Feature Selection, Machine Learning and Deep Learning Algorithms on Multi-modal Omics Data
Index