Medical Books: Algorithms

Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R

Author: Gabriel Valiente
Edition:
Publisher: Chapman and Hall/CRC
Binding: Hardcover
ISBN: 142006973X

Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R (Chapman & Hall/CRC Mathematical & Computational Biology)

Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. Medical books Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R . It implements the algorithms in Perl and R, two widely used scripting languages in computational biology.

The book provides a well-rounded explanation of traditional issues as well as an up-to-date account of more recent developments, such as graph similarity and search. It is organized around the specific algorithmic problems that arise when dealing with structures that are commonly found in computational biology, including biological sequences, trees, and graphs. For each of these structures, the author makes a clear distinction between problems that arise in the analysis of one structure and in the comparative analysis of two or more structures Medical books Combinatorial Pattern Matching Algorithms in Computational Biology using Perl and R. Categories: Pattern formation (Biology), Computational Biology, Perl (Computer program language). Contributors: Gabriel Valiente - Author. Format: Hardcover

Download link for Combinatorial pattern matching algorithms in computational biology using Perl and R

Categories: Pattern formation (Biology), Computational Biology, Perl (Computer program language). Contributors: Gabriel Valiente - Author. Format: Hardcover

Medical Book Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R

It implements the algorithms in Perl and R, two widely used scripting languages in computational biology.

The book provides a well-rounded explanation of traditional issues as well as an up-to-date account of more recent developments, such as graph similarity and search. It is organized around the specific algorithmic problems that arise when dealing with structures that are commonly found in computational biology, including biological sequences, trees, and graphs. For each of these structures, the author makes a clear distinction between problems that arise in the analysis of one structure and in the comparative analysis of two or more structures. He also presents phylogenetic trees and networks as examples of trees and graphs in computational biology.

This book supplies a comprehensive view of the whole field of combinatorial pattern matching from a computational biology perspective. Along with thorough discussions of each biological problem, it includes detailed algorithmic solutions in pseudo-code, full Perl and R implementation, and pointers to other software, such as those on CPAN and CRAN.

An Introduction to Genetic Algorithms

Author: Melanie Mitchell
Edition:
Publisher: The MIT Press
Binding: Hardcover
ISBN: 0262133164

An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. Medical books An Introduction to Genetic Algorithms . This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics— particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline Medical books .

Medical Book An Introduction to Genetic Algorithms

This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics— particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.

The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.