Download Free Biological Pattern Discovery With R Machine Learning Approaches Book in PDF and EPUB Free Download. You can read online Biological Pattern Discovery With R Machine Learning Approaches and write the review.

This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Due to its capability of handling very complex problems and its high flexibility in adapting to different algorithms, the kernel machine plays a crucial role in machine learning.Bio-Kernel Machines and Applications will introduce a new type of kernel machine for the exploration and modeling between the genotypic inherent structures of short protein sequences or nucleic sequences and the phenotypic biological properties or functions of proteins or nucleotides.The book seeks to establish the fundamentals of the bio-kernel machines by presenting the basic principle and theory of the kernel machine and the various formats of kernel machines, such as string kernel machines adapted for biological applications. The book will also introduce several biological applications of the mutation matrices, demonstrating how mutation matrices can enhance the efficiency and biological relevance of machine learning models applied in specific biological problems.Through analyzing current applications of bio-kernel machines, readers will delve into the advantages of the bio-kernel machines and explore how bio-kernel machines can be further enhanced to tackle a wide spectrum of biological challenges and pave the way for future advancements.
As the first volume of World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence, this volume is designed to lay the foundation for the understanding of the Semantic Computing (SC), as a core concept to study Robotic Intelligence in the subsequent volumes.This volume aims to provide a reference to the development of Semantic Computing, in the terms of 'meaning', 'context', and 'intention'. It brings together a series of technical notes, in average, no longer than 10 pages in length, each focuses on one topic in Semantic Computing; being review article or research paper, to explain the fundamental concepts, models or algorithms, and possible applications of the technology concerned.This volume will address three core areas in Semantic Computing:
This book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.
“Frontiers in Anti-Cancer Drug Discovery” is an Ebook series devoted to publishing the latest and the most important advances in Anti-Cancer drug design and discovery. Eminent scientists write contributions on all areas of rational drug design and drug discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, recent important patents, and structure-activity relationships. The Ebook series should prove to be of interest to all pharmaceutical scientists involved in research in Anti-Cancer drug design and discovery. Each volume is devoted to the major advances in Anti-Cancer drug design and discovery. The Ebook series is essential reading to all scientists involved in drug design and discovery who wish to keep abreast of rapid and important developments in the field. The fifth volume of the series features chapters on the following topics: -Nutraceuticals and natural food products for cancer treatment -Pharmacogenomics in Anti-cancer treatment -Cancer stem cells -Potassium channel targeting for brain tumor treatment -Sorafenib in the management of hepatocellular carcinoma …and more.
"This book discusses advances in modern data mining research in today's rapidly growing global and technological environment"--Provided by publisher.
Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences. - Written by leading academic and industry scientists who have contributed significantly to the field and are at the forefront of artificial neural network (ANN) research - Focuses on ANN in drug design, discovery and delivery, as well as adopted methodologies and their applications to the treatment of various diseases and disorders - Chapters cover important topics across the pharmaceutical process, such as ANN in structure-based drug design and the application of ANN in modern drug discovery - Presents the future potential of ANN-based strategies in biomedical image analysis and much more