Machine Learning in Biological Sciences: Updates and Future Prospects
商品資訊
ISBN13：9789811688805
出版社：Springer Nature
作者：Shyamasree Ghosh
出版日：2022/06/05
裝訂：精裝
規格：23.4cm*15.6cm*2.1cm (高/寬/厚)
商品簡介
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 Learning10. 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 Biology15. 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 Medicine17. 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 analysisApplication 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 cha
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