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Machine learning in biological sciencesupdates and future prospects /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in biological sciencesby Shyamasree Ghosh, Rathi Dasgupta.
Reminder of title:	updates and future prospects /
Author:	Ghosh, Shyamasree.
other author:	Dasgupta, Rathi.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xxi, 336 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-16-8881-2
ISBN:	9789811688812$q(electronic bk.)

Machine learning in biological sciencesupdates and future prospects /
Ghosh, Shyamasree.

Machine learning in biological sciencesupdates and future prospects /[electronic resource] :by Shyamasree Ghosh, Rathi Dasgupta. - Singapore :Springer Nature Singapore :2022. - xxi, 336 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811688812$q(electronic bk.)

Standard No.: 10.1007/978-981-16-8881-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: QH324.25 / .G46 2022

Dewey Class. No.: 570.285631

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505 0 $a 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.
520 $a 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.
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