Integer Linear Programming in Computational and Systems Biology PDF Download
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Author: Dan Gusfield
Publisher: Cambridge University Press
ISBN: 1108421768
Category : Computers
Languages : en
Pages : 431
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Book Description
This hands-on tutorial text for non-experts demonstrates biological applications of a versatile modeling and optimization technique.
Author: Dan Gusfield
Publisher: Cambridge University Press
ISBN: 1108421768
Category : Computers
Languages : en
Pages : 431
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Book Description
This hands-on tutorial text for non-experts demonstrates biological applications of a versatile modeling and optimization technique.
Author: Dan Gusfield
Publisher: Cambridge University Press
ISBN: 1108386253
Category : Computers
Languages : en
Pages : 431
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Book Description
Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying software (in Python and Perl) demonstrate the concepts.
Author: Saul I. Gass
Publisher: Courier Corporation
ISBN: 048643284X
Category : Mathematics
Languages : en
Pages : 545
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Book Description
Comprehensive, well-organized volume, suitable for undergraduates, covers theoretical, computational, and applied areas in linear programming. Expanded, updated edition; useful both as a text and as a reference book. 1995 edition.
Author: John W. Chinneck
Publisher: Springer Science & Business Media
ISBN: 0387749322
Category : Mathematics
Languages : en
Pages : 283
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Book Description
Written by a world leader in the field and aimed at researchers in applied and engineering sciences, this brilliant text has as its main goal imparting an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. It includes algorithms on seeking feasibility and analyzing infeasibility, as well as describing new and surprising applications.
Author: L. Pachter
Publisher: Cambridge University Press
ISBN: 9780521857000
Category : Mathematics
Languages : en
Pages : 440
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Book Description
This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.
Author: Jon Lee
Publisher: Springer Science & Business Media
ISBN: 1461419271
Category : Mathematics
Languages : en
Pages : 687
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Book Description
Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.
Author: Aidong Zhang
Publisher: Cambridge University Press
ISBN: 1139479032
Category : Computers
Languages : en
Pages : 283
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Book Description
The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Proteins seldom act as single isolated species; rather, proteins involved in the same cellular processes often interact with each other. Functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.
Author: Tandy Warnow
Publisher: Springer
ISBN: 3030108376
Category : Computers
Languages : en
Pages : 426
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Book Description
This volume presents a compelling collection of state-of-the-art work in algorithmic computational biology, honoring the legacy of Professor Bernard M.E. Moret in this field. Reflecting the wide-ranging influences of Prof. Moret’s research, the coverage encompasses such areas as phylogenetic tree and network estimation, genome rearrangements, cancer phylogeny, species trees, divide-and-conquer strategies, and integer linear programming. Each self-contained chapter provides an introduction to a cutting-edge problem of particular computational and mathematical interest. Topics and features: addresses the challenges in developing accurate and efficient software for the NP-hard maximum likelihood phylogeny estimation problem; describes the inference of species trees, covering strategies to scale phylogeny estimation methods to large datasets, and the construction of taxonomic supertrees; discusses the inference of ultrametric distances from additive distance matrices, and the inference of ancestral genomes under genome rearrangement events; reviews different techniques for inferring evolutionary histories in cancer, from the use of chromosomal rearrangements to tumor phylogenetics approaches; examines problems in phylogenetic networks, including questions relating to discrete mathematics, and issues of statistical estimation; highlights how evolution can provide a framework within which to understand comparative and functional genomics; provides an introduction to Integer Linear Programming and its use in computational biology, including its use for solving the Traveling Salesman Problem. Offering an invaluable source of insights for computer scientists, applied mathematicians, and statisticians, this illuminating volume will also prove useful for graduate courses on computational biology and bioinformatics.
Author: Veli Mäkinen
Publisher: Cambridge University Press
ISBN: 1009341219
Category : Computers
Languages : en
Pages : 470
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Book Description
Guided by standard bioscience workflows in high-throughput sequencing analysis, this book for graduate students, researchers, and professionals in bioinformatics and computer science offers a unified presentation of genome-scale algorithms. This new edition covers the use of minimizers and other advanced data structures in pangenomics approaches.
Author: Jeff Edmonds
Publisher: Cambridge University Press
ISBN: 1139471759
Category : Computers
Languages : en
Pages : 409
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Book Description
This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof. Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.
