What Is Codon
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What is Expression?
What are the elements of expression? What are the origins, aims, and functions of expression? An adequate theory of expression can help us to address these questions and to recognize the diversity of the many modes of expression (scientific, ethical, aesthetic, religious, and sociocultural). Alex Scott describes the interdependence of the modes of expression, showing that a theory of expression can promote social understanding by illuminating the nature of our interdependence as individuals in society. Expression theory, as described by Scott, is not merely a theory of art. It is a theory of the ethics, aesthetics, psychology, logic, language, and politics of expression. It is a theory that enables us to examine in a more comprehensive way the question of whether there are any logical limits to the expressive capacity of language. Expression theory is also a theory that enables us transcend the dialectics of the said and the unsaid, the sayable and the unsayable. It enables us to address the question of whether the communicability of a person's thoughts or feelings is determined solely by that person's communicative competence or whether there are some kinds of thoughts and feelings that are truly ineffable and incommunicable.
Informatics and Machine Learning
Author: Stephen Winters-Hilt
language: en
Publisher: John Wiley & Sons
Release Date: 2022-01-06
Informatics and Machine Learning Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work. The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author’s teaching and industry experience. A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes’ rule An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.
Introduction to Computational Genomics
Author: Nello Cristianini
language: en
Publisher: Cambridge University Press
Release Date: 2006-12-14
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.