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The sequencing of the human genome involved thousands of scientists but used relatively few tools. Obtaining sequences is simpler, but aligning the sequences remains a complicated but underappreciated aspect of comparative molecular biology. This book discusses the practice of alignment, and the procedures by which alignments are established.
Running to almost 400 pages, and featuring more than 40 papers, this work on combinatorial optimization and applications will be seen as an important addition to the literature. It constitutes the refereed proceedings of the first International Conference on Combinatorial Optimization and Applications, COCOA 2007, held in Xi'an, China in August of that year. The 29 revised full papers presented together with 8 invited papers and 2 invited presentations were carefully reviewed and selected from 114 submissions and cover both theoretical issues and practical applications.
Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions Developed from the author's own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the bi
SCIENCE MEETS SPIRITUALITY: A theoretical physicist helps you go beyond simplified self-help practices to harness synchronicity and flow—so you can live better, work smarter, and find purpose in your life. When we align with circumstance, circumstance aligns with us. Using a cutting-edge scientific theory of synchronicity, Sky Nelson-Isaacs presents a model for living “in the flow”—a state of optimal functioning, creative thinking, and seemingly effortless productivity. Nelson-Isaacs explains how our choices create meaning, translating current and original ideas from theoretical physics and quantum mechanics into accessible, actionable steps that we can all take to live lives in better alignment with who we are and who we want to be. By turns encouraging and empowering, Living in Flow helps us develop an informed relationship to meaning-making and purposefulness in our lives. From this we can align ourselves more effectively within our personal, professional, and community relationships to live more in flow.
According to Putnam to talk of “facts” without specifying the language to be used is to talk of nothing; “object” itself has many uses and as we creatively invent new uses of words “we find that we can speak of ‘objects’that were not ‘values of any variable’in 1 any language we previously spoke” . The notion of object becomes, then, like the notion of reference, a sort of open land, an unknown territory. The exploration of this land - pears to be constrained by use and invention. But, we may wonder, is it possible to guide invention and control use? In what way, in particular, is it possible, at the level of na- ral language, to link together program expressions and natural evolution? To give an answer to these onerous questions we should immediately point out that cognition (as well as natural language) has to be considered first of all as a peculiar fu- tion of active biosystems and that it results from complex interactions between the - ganism and its surroundings. “In the moment an organism perceives an object of wh- ever kind, it immediately begins to ‘interpret’this object in order to react properly to it . . . It is not necessary for the monkey to perceive the tree in itself. . . What counts is sur- 2 vival” .
This book introduces a new theory in Computer Vision yielding elementary techniques to analyze digital images. These techniques are a mathematical formalization of the Gestalt theory. From the mathematical viewpoint the closest field to it is stochastic geometry, involving basic probability and statistics, in the context of image analysis. The book is mathematically self-contained, needing only basic understanding of probability and calculus. The text includes more than 130 illustrations, and numerous examples based on specific images on which the theory is tested. Detailed exercises at the end of each chapter help the reader develop a firm understanding of the concepts imparted.
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.
This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Principles of Proteomics is designed specifically to explain the different stages of proteomic analysis, their complexities and their jargon to students and researchers in a non-technical overview of the field. The author describes the broad range of problems which proteomics can address, including structural proteomics, interaction proteomics, protein modification analysis and functional proteomics. Methodologies are described in user-friendly language, from the more traditional two-dimensional gel electrophoresis to the new developments in protein chip technologies. These are well presented in the context of overall strategies which can be adopted to address the different aspects of large-scale protein analysis.