Rna Computational Methods For Structure Kinetics And Rational Design Volume One
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RNA: Computational Methods for Structure, Kinetics, and Rational Design: Volume One
Comprising two volumes, RNA: Computational Methods for Structure, Kinetics, and Rational Design is a comprehensive treatment of computational methods concerning the secondary structure, folding kinetics and rational design of RNA. Volume One concerns energy and structure and is divided into five chapters. Chapter 1 describes the molecular structure of ribonucleotides, basic classes of RNA and databases of RNA sequences and structure. Chapter 2 presents the basic concepts of thermodynamics, since thermodynamics-based algorithms constitute an essential tool in rational design of functional RNA molecules. Chapter 3 describes how empirical secondary structure energy parameters are obtained from ultraviolet absorbance experiments via Van 't Hoff plots and least-squares data fitting. Chapter 4 describes methods from combinatorics, automata and formal language theory, and complex analysis. Chapter 5 provides an overview of some of the most important thermodynamics-based algorithms related to secondary structure. Exercises and solutions are provided at the end of every chapter and source code is available at the book's website (sometimes including computer programs using Python and extensions Numpy and Scipy). This book provides the nuts, bolts and tools to take the next steps in computational RNA synthetic biology. It is perfect for advanced undergraduate, graduate and post-graduate readers having analytical interests and skills from areas such as physical chemistry, physics, mathematics, computer science, and statistics.
Machine Learning in Bioinformatics
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Advances in Protein Molecular and Structural Biology Methods
Advances in Protein Molecular and Structural Biology Methods offers a complete overview of the latest tools and methods applicable to the study of proteins at the molecular and structural level. The book begins with sections exploring tools to optimize recombinant protein expression and biophysical techniques such as fluorescence spectroscopy, NMR, mass spectrometry, cryo-electron microscopy, and X-ray crystallography. It then moves towards computational approaches, considering structural bioinformatics, molecular dynamics simulations, and deep machine learning technologies. The book also covers methods applied to intrinsically disordered proteins (IDPs)followed by chapters on protein interaction networks, protein function, and protein design and engineering. It provides researchers with an extensive toolkit of methods and techniques to draw from when conducting their own experimental work, taking them from foundational concepts to practical application. - Presents a thorough overview of the latest and emerging methods and technologies for protein study - Explores biophysical techniques, including nuclear magnetic resonance, X-ray crystallography, and cryo-electron microscopy - Includes computational and machine learning methods - Features a section dedicated to tools and techniques specific to studying intrinsically disordered proteins