Machine learning in biological sciences updates and future prospects - Ghosh, Shyamasree; Dasgupta, Rathi (Springer, 2022)
1. Overview of machine learning applications in biology; 2. Machine Learning Methods; I. Associations,; II. Classification,; III. Regression,; IV. Unsupervised learning,; V. Reinforcement learning,; Introduction to the Machine Learning Models; 3. Model selection and generalization,; 4. Multivariate Methods, ; 5. Dimensional Reduction, ; 6. Clustering (K-means, Adaptive Resonance Theory, Self Organizing Maps), ; 7. Kernel Machines, ; 8. Hidden Markov Model (HMM); 9. Neural nets and Deep Learning; 10. Bayesian Theory for machine learning, ; 11. Ethics in machine learning and artificial intelligence; ; Using Machine learning methods in Life Sciences ; 12. Different Machine learning models and their appropriate usages; 13. Machine learning and its use in understanding Life Sciences, ; 14.
- Supervised and unsupervised learning, neural networks and deep learning methods in Biology ; 15. Recognizing phenotypes using machine learning ; 16. Reinforcement learning and Support vector machines and random forests in Biological processes; Machine Learning: Software and Applications used in Biology and Medicine ; 17. The Cloud, Microsoft, Google, Facebook applications in healthcare; 18. Applications and software of machine learning and artificial intelligence in medical knowledge in One Health ; 19. Medical Health Approaches cloud set up,; 20. Life Sciences in Azure and Amazon Web Services; ; Application of ML in detection of Toxicity ; 21. Toxicity: An Introduction (drug toxicity and molecule-molecule interactions); 22. Machine learning and Toxicity Studies; Application in Human life ; 23. Applications of machine learning in study of cell biology, ; 24. Genetics using unsupervised learning methods such as KNN, ; 25.
- Cell Fate analysis using PCA or similar dimensionality reduction methods, ; 26. Detection of disease through biomarker data and image analysis; Application in Animal sciences ; 27. Animal Behaviour: An Introduction; 28. Study of animal behaviour by conventional methods and bottlenecks and advantages of machine learning; 29. Machine learning and study of precision animal agriculture and animal husbandry ; 30. Machine learning in the study of animal health and veterinary sciences; 31. Machine learning in identification of animal viral reservoirs.; Application in Plants ; 32. Problems in Plant Biology that are yet to be tackled; 33. Machine learning in agriculture,; 34. Machine learning in understanding of plant pathogen interactions, ; 35. Machine learning in plant disease research.; Challenges and Road Ahead ; 36. BioRobotics ; A. An Introduction; B. BioRobots in detection, identification, prevention and treatment of disease at molecular level; 37.
- The challenges to application of machine learning in biological sciences; 38. The future of machine learning