Machine learning in biological sciences: updates and future prospects
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,...
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer
[2022]
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | 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 |
| Beschreibung: | xxi, 336 Seiten Illustrationen 705 grams |
| ISBN: | 9789811688805 |
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| 500 | |a - 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. | ||
| 500 | |a - 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. | ||
| 500 | |a - The challenges to application of machine learning in biological sciences; 38. 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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Datensatz im Suchindex
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| author | Ghosh, Shyamasree Dasgupta, Rathi |
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| spelling | Ghosh, Shyamasree Verfasser (DE-588)1174649275 aut Machine learning in biological sciences updates and future prospects Shyamasree Ghosh, Rathi Dasgupta Singapore Springer [2022] xxi, 336 Seiten Illustrationen 705 grams txt rdacontent n rdamedia nc rdacarrier 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 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 bicssc bisacsh Bioinformatics Machine learning Biotechnology Hardcover, Softcover / Medizin/Allgemeines Dasgupta, Rathi Verfasser aut Erscheint auch als Online-Ausgabe 978-981-16-8881-2 |
| spellingShingle | Ghosh, Shyamasree Dasgupta, Rathi Machine learning in biological sciences updates and future prospects bicssc bisacsh Bioinformatics Machine learning Biotechnology |
| title | Machine learning in biological sciences updates and future prospects |
| title_auth | Machine learning in biological sciences updates and future prospects |
| title_exact_search | Machine learning in biological sciences updates and future prospects |
| title_exact_search_txtP | Machine learning in biological sciences updates and future prospects |
| title_full | Machine learning in biological sciences updates and future prospects Shyamasree Ghosh, Rathi Dasgupta |
| title_fullStr | Machine learning in biological sciences updates and future prospects Shyamasree Ghosh, Rathi Dasgupta |
| title_full_unstemmed | Machine learning in biological sciences updates and future prospects Shyamasree Ghosh, Rathi Dasgupta |
| title_short | Machine learning in biological sciences |
| title_sort | machine learning in biological sciences updates and future prospects |
| title_sub | updates and future prospects |
| topic | bicssc bisacsh Bioinformatics Machine learning Biotechnology |
| topic_facet | bicssc bisacsh Bioinformatics Machine learning Biotechnology |
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