Artificial Intelligence Technologies for Computational Biology

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.

Ranjeet Kumar Rout is currently serving as an assistant professor, department of computer science and engineering, National Institute of Technology Srinagar, India. Prior to working at NIT Srinagar, Dr. Ranjeet had research and teaching experience from Amity University Noida, National Institute Technology Jalandhar, and Indian Statistical Institute (ISI) Kolkata, India. His research interests include machine learning, and deep learning, visual cryptography, and computational biology. He has published papers in peer-reviewed international and scientific journals in the field of non-linear Boolean functions and computational biology. Saiyed Umer is presently working as an assistant professor in, department of computer science and engineering, Aliah University, Kolkata, India. He was the Research Personnel at Indian Statistical Institute (ISI), Kolkata, India, from November 2012 to April 2017. His research interests include biometric, computer vision, machine learning, video analysis, and deep learning. He has published research papers in journals of national and international repute. Sabha Sheikh is currently working as an assistant professor in, department of computer science and engineering, National Institute of Technology Srinagar, India. She has published papers in conferences and journals. Her research interests include machine learning, deep learning, image processing, and computational biology. A. L. Sangal is presently working as a professor (HAG) & Head, department of computer science and engineering, Dr. B R Ambedkar National Institute of Technology Jalandhar, Punjab, India. He has published more than 36 papers in national and international journals of repute, several papers in international conferences and two book chapters. His research interests include scientific computing, Computing Networks, information security, and cloud computing.

1. Graph Representation Learning for Protein Classification 2. Prediction of Methylation Sites in Protein Sequences 3. A Taxonomy of e-Healthcare Techniques and Solutions 4. Classification of Lung Diseases Using Machine Learning Techniques 5. Multi-Objective Bacterial Foraging Optimization 6. Artificial Intelligence for Biomedical Informatics 7. A Novel Approach for Feature Selection Using Artificial Neural Networks and Particle Swarm Optimization. 8. DL-based Diabetic Retinopathy Stage Classification from Retinal Fundus Images 9. Cancer Diagnosis from Histopathology Images Using Deep Learning: A Review 10. Skin Lesion Classification by Using Deep Tree-CNNPrakash Choudhary and Sameer Mansuri 11. Diagnose Covid-19 On Its Early Stages Using Lung CT Images 12. Impact of Machine Learning Practices on Biomedical Informatics 13. Recognition of Types of Arrhythmia 14. ML and DL Algorithms on Multi-Modal Omics Data.