Machine Learning In Bioinformatics PDF Download
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| Author | : Yanqing Zhang |
| Publisher | : John Wiley & Sons |
| Total Pages | : 476 |
| Release | : 2009-02-23 |
| Genre | : Computers |
| ISBN | : 0470397411 |
Download Machine Learning in Bioinformatics Book in PDF, ePub and Kindle
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel 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. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
| 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 | : 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 | : 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 | : Debmalya Barh |
| Publisher | : CRC Press |
| Total Pages | : 721 |
| Release | : 2013-03-26 |
| Genre | : Medical |
| ISBN | : 1466562811 |
Download OMICS Book in PDF, ePub and Kindle
With the advent of new technologies and acquired knowledge, the number of fields in omics and their applications in diverse areas are rapidly increasing in the postgenomics era. Such emerging fields—including pharmacogenomics, toxicogenomics, regulomics, spliceomics, metagenomics, and environomics—present budding solutions to combat global challenges in biomedicine, agriculture, and the environment. OMICS: Applications in Biomedical, Agricultural, and Environmental Sciences provides valuable insights into the applications of modern omics technologies to real-world problems in the life sciences. Filling a gap in the literature, it offers a broad, multidisciplinary view of current and emerging applications of omics in a single volume. Written by highly experienced active researchers, each chapter describes a particular area of omics and the associated technologies and applications. Topics covered include: Proteomics, epigenomics, and pharmacogenomics Toxicogenomics and the assessment of environmental pollutants Applications of plant metabolomics Nutrigenomics and its therapeutic applications Microalgal omics and omics approaches in biofuel production Next-generation sequencing and omics technology for transgenic plant analysis Omics approaches in crop improvement Engineering dark-operative chlorophyll synthesis Computational regulomics Omics techniques for the analysis of RNA splicing New fields, including metagenomics, glycomics, and miRNA Breast cancer biomarkers for early detection Environomics strategies for environmental sustainability This timely book explores a wide range of omics application areas in the biomedical, agricultural, and environmental sciences. Throughout, it highlights working solutions as well as open problems and future challenges. Demonstrating the diversity of omics, it introduces readers to state-of-the-art developments and trends in omics-driven research.
| Author | : Jason T. L. Wang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 340 |
| Release | : 2006-03-30 |
| Genre | : Computers |
| ISBN | : 1846280591 |
Download Data Mining in Bioinformatics Book in PDF, ePub and Kindle
Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.
| Author | : Mario Cannataro |
| Publisher | : Elsevier |
| Total Pages | : 270 |
| Release | : 2022-05-12 |
| Genre | : Computers |
| ISBN | : 0128229292 |
Download Artificial Intelligence in Bioinformatics Book in PDF, ePub and Kindle
Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences Brings readers up-to-speed on current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications
| 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 | : 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 | : Sanghamitra Bandyopadhyay |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 320 |
| Release | : 2007-05-17 |
| Genre | : Computers |
| ISBN | : 3540496076 |
Download Classification and Learning Using Genetic Algorithms Book in PDF, ePub and Kindle
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.
