State-of-the-art statistical methodologies in genomics, proteomics, epigenetics and metabolomics Multiple chapters on protein-protein interaction and next-generation sequencing Each chapter provides a guide to the existing software packages available for implementing the methodologies introduced in it or, if there is none, supplies the code Plenty of examples and illustrative diagrams to help understand the key concepts An accompanying website containing useful additional resources such as links to some of the datasets and databases mentioned in the book, as well as some computer codes

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.

A technically precise yet clear presentation of modern sequential methodologies having immediate applications to practical problems in the real world, Applied Sequential Methodologies communicates invaluable techniques for data mining, agricultural science, genetics, computer simulation, finance, clinical trials, sonar signal detection, randomization, multiple comparisons, psychology, tracking, surveillance, and numerous additional areas of application. Includes more than 500 references, 165 figures and tables, and over 25 pages of subject and author indexes. Applied Sequential Methodologies brings the crucial nature of sequential approaches up to speed with recent theoretical gains, demonstrating their utility for solving real-life problems associated with Change-point detection in multichannel and distributed systems Best component selection for multivariate distributions Multistate processes Approximations for moving sums of discrete random variables Interim and terminal analyses of clinical trials Adaptive designs for longitudinal clinical trials Slope estimation in measurement-error models Tests for randomization and target tracking Appropriate count of simulation runs Stock price models Orders of genes Size and power control in multiple comparisons Authored by 33 leading scientists, this volume will greatly benefit sequential analysts, data analysts, applied statisticians, biometricians, clinical trialists, and upper-level undergraduate and graduate students in these disciplines.

"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.

A technically precise yet clear presentation of modern sequential methodologies having immediate applications to practical problems in the real world, Applied Sequential Methodologies communicates invaluable techniques for data mining, agricultural science, genetics, computer simulation, finance, clinical trials, sonar signal detection, randomization, multiple comparisons, psychology, tracking, surveillance, and numerous additional areas of application. Includes more than 500 references, 165 figures and tables, and over 25 pages of subject and author indexes. Applied Sequential Methodologies brings the crucial nature of sequential approaches up to speed with recent theoretical gains, demonstrating their utility for solving real-life problems associated with -Change-point detection in multichannel and distributed systems -Best component selection for multivariate distributions -Multistate processes -Approximations for moving sums of discrete random variables -Interim and terminal analyses of clinical trials -Adaptive designs for longitudinal clinical trials -Slope estimation in measurement-error models -Tests for randomization and target tracking -Appropriate count of simulation runs -Stock price models -Orders of genes -Size and power control in multiple comparisons Authored by 33 leading scientists, this volume will greatly benefit sequential analysts, data analysts, applied statisticians, biometricians, clinical trialists, and upper-level undergraduate and graduate students in these disciplines.