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The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
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.
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, em style="font-family: Calibri, sans-serif; font-size: 11pt;"Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: •Basic classification and regression with perceptrons •Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training •Multi-Layer Perceptrons for learning from descriptors, and de-noising data •Recurrent neural networks for learning from sequences •Convolutional neural networks for learning from images •Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: • Basic classification and regression with perceptrons • Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training • Multi-Layer Perceptrons for learning from descriptors, and de-noising data • Recurrent neural networks for learning from sequences • Convolutional neural networks for learning from images • Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource.
An emerging, ever-evolving branch of science, bioinformatics has paved the way for the explosive growth in the distribution of biological information to a variety of biological databases, including the National Center for Biotechnology Information. For growth to continue in this field, biologists must obtain basic computer skills while computer spe
Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"
Key features include Margaret Atwood’s essay ‘Are Humans Necessary?’ tracing the history of robots in literature and culture; a fictional piece written by the late cultural theorist Mark Fisher in collaboration co-curator Suzanne Livingston; xenopoet Amy Ireland and computer generated 3D poems/ ‘modules’ that pose a challenge to the limitations of human language and Demis Hassabis, co-founder of Google DeepMind, and professional Go player, Fan Hui, describe how their experience of the Alpha Go program changed their perceptions of human vs artificial intelligence.
An exhaustive work that represents a landmark exploration of both the philosophical and methodological issues surrounding the search for true artificial intelligence. Distinguished psychologists, computer scientists, philosophers, and programmers from around the world debate weighty issues such as whether a self-conscious computer would create an internet ‘world mind’. This hugely important volume explores nothing less than the future of the human race itself.
Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.
Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential.
Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design.