Machine Learning in Biological Sciences (eBook)

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.

Dr. Shyamasree Ghosh is working as a Scientific Officer at the NISER Bhubaneswar, India. She has worked and published extensively in the domain of glycobiology, sialic acids, immunology, stem cells, nanotechnology, and computational immunology. She has graduated from the prestigious Presidency College Kolkata in 1998, and was awarded the prestigious National Scholarship from the Government of India. She completed her masters in Biotechnology from Calcutta University in 2000, ranking second in the University. She did her PhD from the Indian institute of Chemical Biology (IICB) Kolkata, CSIR, India in glycobiology, sialic acids, immunology, and Cancer Biology. She did her Post Doctoral Research in Indian Association for the Cultivation of Sciences (IACS), India on nanotechnology, stem cells and Cancer Biology. She has served as faculty and Chair (2005-2009) of Dept. Her work has been recognised and accepted globally and she has been awarded by different scientific bodies in India. She is a member of different National Science Bodies and is  Editorial Board member in Scientific Societies.

Dr. Rathi Dasgupta, has been working in the computer science based industry since the last 25 years and is currently the SVP, Intelliswift Software Inc., Newark, CA, USA. He did his bachelor in Science with Major in Physics, St. Xavier's College, University of Calcutta, Calcutta, India, (Integrated MTech), radio physics & electronics, Institute of Radio Physics and Electronics,  Master of Science (MS), Nuclear & Particle Physics, University College of Science & Technology, doctoral research, in theoretical physics, Saha Institute of Nuclear Physics, University of Calcutta and has been  adjunct Faculty, Mathematics & Computer Science at MS in CSE & EE Class, Alliance University, visiting faculty, Computer Information Science, MBA Class, Indian Institute of Management, Bangalore and Associate Visiting Professor, Computer Science & Mathematics, Xavier Institute of Management & Entrepreneurship,.? He also have few US provisional and full patents in Machine Learning.

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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.