Machine Learning in Biological Sciences: Updates and Future Prospects

By Shyamasree Ghosh and Rathi Dasgupta

Book Overview

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 cha

• Format: Hardcover
• Language: English
• ISBN: 981168880X
• ISBN13: 9789811688805
• Release Date: May 2022
• Publisher: Springer
• Length: 336 Pages
• Weight: 1.50 lbs.
• Dimensions: 0.8" x 6.1" x 9.2"