Bioinformatics The Machine Learning Approach PDF Download
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Author | : Pierre Baldi |
Publisher | : MIT Press (MA) |
Total Pages | : 351 |
Release | : 1998 |
Genre | : Biomolecules |
ISBN | : 9780262024426 |
Download Bioinformatics Book in PDF, ePub and Kindle
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
Author | : Pierre Baldi |
Publisher | : MIT Press |
Total Pages | : 492 |
Release | : 2001-07-20 |
Genre | : Computers |
ISBN | : 9780262025065 |
Download Bioinformatics, second edition Book in PDF, ePub and Kindle
A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
Author | : Rabinarayan Satpathy |
Publisher | : John Wiley & Sons |
Total Pages | : 433 |
Release | : 2021-01-20 |
Genre | : Computers |
ISBN | : 111978560X |
Download Data Analytics in Bioinformatics Book in PDF, ePub and Kindle
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Author | : Sushmita Mitra |
Publisher | : CRC Press |
Total Pages | : 386 |
Release | : 2008-06-05 |
Genre | : Mathematics |
ISBN | : 1420011782 |
Download Introduction to Machine Learning and Bioinformatics Book in PDF, ePub and Kindle
Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.
Author | : Pierre Baldi |
Publisher | : |
Total Pages | : 452 |
Release | : 2001 |
Genre | : Bioinformatics |
ISBN | : |
Download Bioinformatics Book in PDF, ePub and Kindle
Author | : Gianni Russell |
Publisher | : States Academic Press |
Total Pages | : 0 |
Release | : 2023-09-26 |
Genre | : |
ISBN | : 9781639897339 |
Download Bioinformatics: The Machine Learning Approach Book in PDF, ePub and Kindle
Bioinformatics is the application of tools of computation and analysis for capturing and interpreting biological data. Machine learning is a branch of artificial intelligence and computer science that has applications in multiple fields. Machine learning in bioinformatics involves the application of machine learning algorithms to bioinformatics such as proteomics, genomics, microarrays, evolution, text mining and systems biology. Genomics is a prominent area of bioinformatics involved in the study of genome mapping, genomic expression regulation, and genome evolution and editing. In medical diagnostics, some of the major applications of machine learning in genomics are genome sequencing, gene editing, and improving clinical workflow. This book outlines a machine learning approach towards bioinformatics. A number of latest researches have been included to keep the readers updated with the global concepts in this area of study. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.
Author | : Zheng Rong Yang |
Publisher | : World Scientific |
Total Pages | : 462 |
Release | : 2021-09-17 |
Genre | : Science |
ISBN | : 9811240132 |
Download Biological Pattern Discovery With R: Machine Learning Approaches Book in PDF, ePub and Kindle
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.
Author | : K. G. Srinivasa |
Publisher | : Springer Nature |
Total Pages | : 318 |
Release | : 2020-01-30 |
Genre | : Technology & Engineering |
ISBN | : 9811524459 |
Download Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications Book in PDF, ePub and Kindle
This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Author | : Elena Marchiori |
Publisher | : Springer Science & Business Media |
Total Pages | : 311 |
Release | : 2007-04-02 |
Genre | : Computers |
ISBN | : 354071782X |
Download Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
Author | : Zheng Rong Yang |
Publisher | : World Scientific |
Total Pages | : 337 |
Release | : 2010 |
Genre | : Computers |
ISBN | : 981428730X |
Download Machine Learning Approaches to Bioinformatics Book in PDF, ePub and Kindle
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. Furthermore, the book includes R codes and example data sets to help readers develop their own bioinformatics research skills. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics textbooks on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for undergraduate/graduate teaching. An essential textbook for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.