Machine Learning in Biological Sciences: Updates and Future Prospects (Paperback, 2022)

· 제목 : Machine Learning in Biological Sciences: Updates and Future Prospects (Paperback, 2022) 
· 분류 : 외국도서 > 의학 > 의료 서비스 > 의료기술
· ISBN : 9789811688836
· 쪽수 : 336쪽
· 출판일 : 2023-05-06

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