Machine learning in biological sciences : updates and future prospects / Shyamasree Ghosh, Rathi Dasgupta.
Ghosh, Shyamasree.; Dasgupta, Rathi.
2022
QH324.25
Available Online

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Title Machine learning in biological sciences : updates and future prospects / Shyamasree Ghosh, Rathi Dasgupta.
Author Ghosh, Shyamasree.
ISBN 9789811688812 (electronic bk.)
9811688818 (electronic bk.)
981168880X
9789811688805
DOI https://doi.org/10.1007/978-981-16-8881-2
Publication Details Singapore : Springer, 2022.
Language English
Description 1 online resource (337 pages)
Item Number 10.1007/978-981-16-8881-2 doi
Call Number QH324.25
Dewey Decimal Classification 570.285631
Summary This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 17, 2022).
Added Author Dasgupta, Rathi.
Available in Other Form Machine Learning in Biological Sciences.
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Record Appears in Online Resources > Ebooks
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Chapter 1. A Brief Overview Of Applications Of Machine Learning In Life Sciences
Chapter 2. Introduction To Artificial Intelligence (Ai) Methods In Biology
Chapter 3. Machine Learning Methods
Chapter 4. Introduction To Machine Learning Models
Chapter 5. Model Selection Formachine Learning
Chapter 6. Multivariate Methods In Machine Learning In The Context Of Biological Data
Chapter 7. Dimensionality Reduction Methods In Machine Learning
Chapter 8. Hidden Markov Method
Chapter 9. Neural Network And Deep Learning. Chapter 10. Ethics In Machine Learning And Artificial Intelligence
Chapter 11. Machine Learning And Life Sciences
Chapter 12. Machine Learning And Negleced Tropical Diseases- Chapter 13. Machine Learning In Cardiovascular Diseases
Chapter 14. Machine Learning And Diabetes
Chapter 15. Machine Learning And Epilepsy
Chapter 16. The Microsoft, Google, Facebook, Pytorch And Applications In Biology
Chapter 17. Applications And Software Of Machine Learning And Ai In Medical Knowledge In Health
Chapter 18. Cloud Computing Infrastructure In Healthcare Industry
Chapter 19. Amazon Web Services (Aws) And Microsoft Azure In The Domain Of Life Sciences
Chapter 20. Toxicity: An Introduction
Chapter 21. Machine Learning (Ml) And Toxicity Studies. Chapter 22. Applications Of Machine Learning In Study Of Cell Biology
Chapter 23. Genomics And Machine Learning
Chapter 24. Cell Fate Analysis And Machine Learning
Chapter 25. Study Of Biomarker And Machine Learning
Chapter 26. Animal Behaviour: An Introduction
Chapter 27. Study Of Animal Behaviour And Machine Learning
Chapter 28. Machine Learning And Precision Farming
Chapter 29. Machine Learning In The Study Of Animal Health And Veterinary Sciences
Chapter 30. Macinelearning And Animalresorviors
Chapter 31. Challenging Problems In Plant Biology
Chapter 32. Machine Learning And Plant Sciences. Chapter 33. Machine Learning In Understanding Of Plant Pathogen Interactions
Chapter 34. Machine Learning In Plant Disease Research
Chapter 35. Biorobots
Chapter 36. The Challenges To Application Of Machine Learning In Biological Sciences
Chapter 37. The Future Of Machine Learning.

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