Machine Learning In Biological Sciences
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| Author | : Shyamasree Ghosh |
| Publisher | : Springer Nature |
| Total Pages | : 337 |
| Release | : 2022-05-04 |
| Genre | : Medical |
| ISBN | : 9811688818 |
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.
| Author | : Bharath Ramsundar |
| Publisher | : O'Reilly Media |
| Total Pages | : 236 |
| Release | : 2019-04-10 |
| Genre | : Science |
| ISBN | : 1492039802 |
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
| Author | : Pierre Baldi |
| Publisher | : Cambridge University Press |
| Total Pages | : 387 |
| Release | : 2021-07 |
| Genre | : Computers |
| ISBN | : 1108845355 |
Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
| Author | : Davide Bacciu |
| Publisher | : World Scientific Publishing Europe Limited |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Artificial intelligence |
| ISBN | : 9781800610934 |
Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.
| Author | : Shampa Sen |
| Publisher | : CRC Press |
| Total Pages | : 354 |
| Release | : 2018-07-02 |
| Genre | : Bioinformatics |
| ISBN | : 9781138492691 |
This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.
| Author | : Yasha Hasija |
| Publisher | : CRC Press |
| Total Pages | : 299 |
| Release | : 2021-04-08 |
| Genre | : Computers |
| ISBN | : 1000345483 |
Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.
| Author | : Joern Helbert |
| Publisher | : Elsevier |
| Total Pages | : 234 |
| Release | : 2022-03-22 |
| Genre | : Science |
| ISBN | : 0128187220 |
Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice
| Author | : Alan H. Fielding |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 265 |
| Release | : 2012-12-06 |
| Genre | : Science |
| ISBN | : 1461552893 |
This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
| Author | : Lawrence Hunter |
| Publisher | : |
| Total Pages | : 484 |
| Release | : 1993 |
| Genre | : Computers |
| ISBN | : |
These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.
| Author | : Irene L. Hudson |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 525 |
| Release | : 2009-11-24 |
| Genre | : Science |
| ISBN | : 9048133351 |
As climate change continues to dominate the international environmental agenda, phenology – the study of the timing of recurring biological events – has received increasing research attention, leading to an emerging consensus that phenology can be viewed as an ‘early warning system’ for climate change impact. A multidisciplinary science involving many branches of ecology, geography and remote sensing, phenology to date has lacked a coherent methodological text. This new synthesis, including contributions from many of the world’s leading phenologists, therefore fills a critical gap in the current biological literature. Providing critiques of current methods, as well as detailing novel and emerging methodologies, the book, with its extensive suite of references, provides readers with an understanding of both the theoretical basis and the potential applications required to adopt and adapt new analytical and design methods. An invaluable source book for researchers and students in ecology and climate change science, the book also provides a useful reference for practitioners in a range of sectors, including human health, fisheries, forestry, agriculture and natural resource management.
