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Bioinformatics Algorithms
Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as "Are There Fragile Regions in the Human Genome?" or "Which DNA Patterns ...
Biological Modeling
Have you ever wondered why zebras have stripes? Have you ever wondered how your cells can quickly react to their environment and perform complex tasks without intelligence? Have you ever wondered why the original SARS coronavirus fizzled out but SARS-CoV-2 has spread like wildfire around the planet? Have you ever wondered how algorithms can be trained to "see" cells as well as a human?What these questions share is that they can start to be answered by modeling biological systems at multiple "scales" of resolution, from the microscopic to the molecular.In this book, we will explain how to build models of biological systems that are relatively simple but nevertheless provide us with deep, fascinating insights into how those systems operate.For readers interested in getting their hands dirty with biological modeling, we provide tutorials explaining how to build the models used in this book at the website of the course companion: http://biologicalmodeling.org.
Bioinformatics for Biologists
The computational education of biologists is changing to prepare students for facing the complex datasets of today's life science research. In this concise textbook, the authors' fresh pedagogical approaches lead biology students from first principles towards computational thinking. A team of renowned bioinformaticians take innovative routes to introduce computational ideas in the context of real biological problems. Intuitive explanations promote deep understanding, using little mathematical formalism. Self-contained chapters show how computational procedures are developed and applied to central topics in bioinformatics and genomics, such as the genetic basis of disease, genome evolution or the tree of life concept. Using bioinformatic resources requires a basic understanding of what bioinformatics is and what it can do. Rather than just presenting tools, the authors - each a leading scientist - engage the students' problem-solving skills, preparing them to meet the computational challenges of their life science careers.
Algorithms in Bioinformatics
This book constitutes the refereed proceedings of the 13th International Workshop on Algorithms in Bioinformatics, WABI 2013, held in Sophia Antipolis, France, in September 2013. WABI 2013 is one of seven workshops which, along with the European Symposium on Algorithms (ESA), constitute the ALGO annual meeting and highlights research in algorithmic work for bioinformatics, computational biology and systems biology. The goal is to present recent research results, including significant work-in-progress, and to identify and explore directions of future research. The 27 full papers presented were carefully reviewed and selected from 61 submissions. The papers cover all aspects of algorithms in bioinformatics, computational biology and systems biology.
The Algorithm Design Manual
"My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer’s toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google) "Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and so...
Genetics Fundamentals Notes
This up-to-date and comprehensive textbook is essential reading material for advanced undergraduate and graduate students with a course module in genetics and developmental biology. The book provides clear, concise, and rigorous foundational concepts of genetics. It opens with an introductory chapter that provides an overview of genetics. The book includes separate and detailed sections on classical genetics, molecular genetics, and population genetics. It covers basic and foundational principles such as Mendelian genetics, chromosomal theory, transcription, translation, mutation, and gene regulation. It further includes chapters on advanced topics such as molecular genetic techniques, genom...
Biological Sequence Analysis
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.
An Introduction to Bioinformatics Algorithms
An introductory text that emphasizes the underlying algorithmic ideas that are driving advances in bioinformatics. This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems. The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design ...
Analysis of Algorithms
Data Structures & Theory of Computation
