Machine Learning in Biological Sciences: Updates and Future Prospects [Hardcover]

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.

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‘