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44 results in Computational Biology and Bioinformatics

Page 1 of 3

Principles of Computational Modelling in Neuroscience
  • 2nd edition
  • David Sterratt, Bruce Graham, Andrew Gillies, Gaute Einevoll, David Willshaw
  • Published online:
    07 October 2023
    Print publication:
    05 October 2023
  • Taking a step-by-step approach to modelling neurons and neural circuitry, this textbook teaches students how to use computational techniques to understand the nervous system at all levels, using case studies throughout to illustrate fundamental principles. Starting with a simple model of a neuron, the authors gradually introduce neuronal morphology, synapses, ion channels and intracellular signalling. This fully updated new edition contains additional examples and case studies on specific modelling techniques, suggestions on different ways to use this book, and new chapters covering plasticity, modelling extracellular influences on brain circuits, modelling experimental measurement processes, and choosing appropriate model structures and their parameters. The online resources offer exercises and simulation code that recreate many of the book's figures, allowing students to practice as they learn. Requiring an elementary background in neuroscience and high-school mathematics, this is an ideal resource for a course on computational neuroscience.

Genome-Scale Algorithm Design
  • Bioinformatics in the Era of High-Throughput Sequencing
  • 2nd edition
  • Veli Mäkinen, Djamal Belazzougui, Fabio Cunial, Alexandru I. Tomescu
  • Published online:
    28 September 2023
    Print publication:
    12 October 2023
  • Presenting the fundamental algorithms and data structures that power bioinformatics workflows, this book covers a range of topics from the foundations of sequence analysis (alignments and hidden Markov models) to classical index structures (k-mer indexes, suffix arrays, and suffix trees), Burrows–Wheeler indexes, graph algorithms, network flows, and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualizations, and exercises, providing graduate students, researchers, and practitioners with a powerful algorithmic toolkit for the applications of high-throughput sequencing. An accompanying website (www.genome-scale.info) offers supporting teaching material. The second edition strengthens the toolkit by covering minimizers and other advanced data structures and their use in emerging pangenomics approaches.

Integrative Bioinformatics for Biomedical Big Data
  • A No-Boundary Thinking Approach
  • Edited by Xiuzhen Huang, Jason H. Moore, Yu Zhang
  • Published online:
    14 September 2023
    Print publication:
    28 September 2023
  • The volume and complexity of biological and biomedical research continues to grow exponentially with cutting-edge technologies such as high-throughput sequencing. Unfortunately, bioinformatics analysis is often considered only after data have been generated, which significantly limits the ability to make sense of complex big data. This unique book introduces the idea of No-Boundary Thinking (NBT) in biological and biomedical research, which aims to access, integrate, and synthesize data, information, and knowledge from bioinformatics to define important problems and articulate impactful research questions. This interdisciplinary volume brings together a team of bioinformatics specialists who draw on their own experiences with NBT to illustrate the importance of collaborative science. It will help stimulate discussion and application of NBT, and will appeal to all biomedical researchers looking to maximize their use of bioinformatics for making scientific discoveries.

Algorithmic Information Dynamics
  • A Computational Approach to Causality with Applications to Living Systems
  • Hector Zenil, Narsis A. Kiani, Jesper Tegnér
  • Published online:
    11 May 2023
    Print publication:
    25 May 2023
  • Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.

Computational Thinking for Life Scientists
  • Benny Chor, Amir Rubinstein
  • Published online:
    19 August 2022
    Print publication:
    08 September 2022
  • Computational thinking is increasingly gaining importance in modern biology, due to the unprecedented scale at which data is nowadays produced. Bridging the cultural gap between the biological and computational sciences, this book serves as an accessible introduction to computational concepts for students in the life sciences. It focuses on teaching algorithmic and logical thinking, rather than just the use of existing bioinformatics tools or programming. Topics are presented from a biological point of view, to demonstrate how computational approaches can be used to solve problems in biology such as biological image processing, regulatory networks, and sequence analysis. The book contains a range of pedagogical features to aid understanding, including real-world examples, in-text exercises, end-of-chapter problems, colour-coded Python code, and 'code explained' boxes. User-friendly throughout, Computational Thinking for Life Scientists promotes the thinking skills and self-efficacy required for any modern biologist to adopt computational approaches in their research with confidence.

Networks of Networks in Biology
  • Concepts, Tools and Applications
  • Edited by Narsis A. Kiani, David Gomez-Cabrero, Ginestra Bianconi
  • Published online:
    24 February 2021
    Print publication:
    01 April 2021
  • Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

Topological Data Analysis for Genomics and Evolution
  • Topology in Biology
  • Raul Rabadan, Andrew J. Blumberg
  • Published online:
    18 November 2019
    Print publication:
    19 December 2019
  • Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.

Integer Linear Programming in Computational and Systems Biology
  • An Entry-Level Text and Course
  • Dan Gusfield
  • Published online:
    31 May 2019
    Print publication:
    13 June 2019
  • 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.

Analyzing Network Data in Biology and Medicine
  • An Interdisciplinary Textbook for Biological, Medical and Computational Scientists
  • Edited by Nataša Pržulj
  • Published online:
    18 March 2019
    Print publication:
    28 March 2019
  • The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.

The Haskell School of Expression
  • Learning Functional Programming through Multimedia
  • Paul Hudak
  • Published online:
    28 May 2018
    Print publication:
    28 February 2000
  • Functional programming is a style of programming that emphasizes the use of functions (in contrast to object-oriented programming, which emphasizes the use of objects). It has become popular in recent years because of its simplicity, conciseness, and clarity. This book teaches functional programming as a way of thinking and problem solving, using Haskell, the most popular purely functional language. Rather than using the conventional (boring) mathematical examples commonly found in other programming language textbooks, the author uses examples drawn from multimedia applications, including graphics, animation, and computer music, thus rewarding the reader with working programs for inherently more interesting applications. Aimed at both beginning and advanced programmers, this tutorial begins with a gentle introduction to functional programming and moves rapidly on to more advanced topics. Details about progamming in Haskell are presented in boxes throughout the text so they can be easily found and referred to.

Computing for Biologists
  • Python Programming and Principles
  • Ran Libeskind-Hadas, Eliot Bush
  • Published online:
    28 May 2018
    Print publication:
    22 September 2014
  • Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by numerous programming exercises, available at www.cs.hmc.edu/CFB.

Computational Phylogenetics
  • An Introduction to Designing Methods for Phylogeny Estimation
  • Tandy Warnow
  • Published online:
    26 October 2017
    Print publication:
    02 November 2017
  • A comprehensive account of both basic and advanced material in phylogeny estimation, focusing on computational and statistical issues. No background in biology or computer science is assumed, and there is minimal use of mathematical formulas, meaning that students from many disciplines, including biology, computer science, statistics, and applied mathematics, will find the text accessible. The mathematical and statistical foundations of phylogeny estimation are presented rigorously, following which more advanced material is covered. This includes substantial chapters on multi-locus phylogeny estimation, supertree methods, multiple sequence alignment techniques, and designing methods for large-scale phylogeny estimation. The author provides key analytical techniques to prove theoretical properties about methods, as well as addressing performance in practice for methods for estimating trees. Research problems requiring novel computational methods are also presented, so that graduate students and researchers from varying disciplines will be able to enter the broad and exciting field of computational phylogenetics.

Genome-Scale Algorithm Design
  • Biological Sequence Analysis in the Era of High-Throughput Sequencing
  • Veli Mäkinen, Djamal Belazzougui, Fabio Cunial, Alexandru I. Tomescu
  • Published online:
    05 May 2015
    Print publication:
    07 May 2015
  • High-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows–Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.

Python Programming for Biology
  • Bioinformatics and Beyond
  • Tim J. Stevens, Wayne Boucher
  • Published online:
    05 February 2015
    Print publication:
    12 February 2015
  • Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.

Flexible Pattern Matching in Strings
  • Practical On-Line Search Algorithms for Texts and Biological Sequences
  • Gonzalo Navarro, Mathieu Raffinot
  • Published online:
    18 December 2014
    Print publication:
    27 May 2002
  • String matching problems range from the relatively simple task of searching a single text for a string of characters to searching a database for approximate occurrences of a complex pattern. Recent years have witnessed a dramatic increase of interest in sophisticated string matching problems, especially in information retrieval and computational biology. This book presents a practical approach to string matching problems, focusing on the algorithms and implementations that perform best in practice. It covers searching for simple, multiple and extended strings, as well as regular expressions, and exact and approximate searching. It includes all the most significant new developments in complex pattern searching. The clear explanations, step-by-step examples, algorithm pseudocode, and implementation efficiency maps will enable researchers, professionals and students in bioinformatics, computer science, and software engineering to choose the most appropriate algorithms for their applications.

Neuronal Dynamics
  • From Single Neurons to Networks and Models of Cognition
  • Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski
  • Published online:
    05 August 2014
    Print publication:
    24 July 2014
  • What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin–Huxley equations and Hopfield model, as well as modern developments in the field such as generalized linear models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
  • Nello Cristianini, John Shawe-Taylor
  • Published online:
    05 March 2013
    Print publication:
    23 March 2000
  • This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

UNIX and Perl to the Rescue!
  • A Field Guide for the Life Sciences (and Other Data-rich Pursuits)
  • Keith Bradnam, Ian Korf
  • Published online:
    05 November 2012
    Print publication:
    19 July 2012
  • Your research has generated gigabytes of data and now you need to analyse it. You hate using spreadsheets but it is all you know, so what else can you do? This book will transform how you work with large and complex data sets, teaching you powerful programming tools for slicing and dicing data to suit your needs. Written in a fun and accessible style, this step-by-step guide will inspire and inform non-programmers about the essential aspects of Unix and Perl. It shows how, with just a little programming knowledge, you can write programs that could save you hours, or even days. No prior experience is required and new concepts are introduced using numerous code examples that you can try out for yourself. Going beyond the basics, the authors touch upon many broader topics that will help those new to programming, including debugging and how to write in a good programming style.

Biological Sequence Analysis
  • Probabilistic Models of Proteins and Nucleic Acids
  • Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
  • Published online:
    05 September 2012
    Print publication:
    23 April 1998
  • Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Genomics and Bioinformatics
  • An Introduction to Programming Tools for Life Scientists
  • Tore Samuelsson
  • Published online:
    05 August 2012
    Print publication:
    07 June 2012
  • With the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.

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