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"--

1. Graph representation learning for protein classification -- 2. Extration of sequence-based features for prediction of methylation sites in protein sequences -- 3. A taxonomy of e-healthcare techniques and solutions: challenges and future directions -- 4. Classification of lung diseases using machine learning techniques -- 5. Multi objective bacterial foraging optimization: a survey -- 6. Artificail intelligence for biomedical informatics -- 7. A novel approach for feature selection using artifitial neural networks and particle swarm optimization -- 8. In search for the optimal -preprocessing techqnique for deep learning-based diabetic retinopathy stage classification from retial fundus images -- 9. Cancer diagnosis from histopathology images using deep learning: a review -- 10. Skin lesion classification by using deep tree-CNN -- 11. Hybrid deep learning model to diagnose Covid-19 ion its early stages using lung CT images -- 12. Impact of machine learning practices on biomedical informatics, its challenges and future benefits -- 13. Recognition of types of arrhythmia: an implementation of ensembling techniques using ECG beat -- 14. Feature selection, machine learning and deep l…