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Informatics and Machine Learning
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 o...
MacHine-Learning Based Sequence Analysis, Bioinformatics and Nanopore Transduction Detection
This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.
Advances in Computational Biology
Proceedings of The 2009 International Conference on Bioinformatics and Computational Biology in Las Vegas, NV, July 13-16, 2009. Recent advances in Computational Biology are covered through a variety of topics. Both inward research (core areas of computational biology and computer science) and outward research (multi-disciplinary, Inter-disciplinary, and applications) will be covered during the conferences. These include: Gene regulation, Gene expression databases, Gene pattern discovery and identification, Genetic network modeling and inference, Gene expression analysis, RNA and DNA structure and sequencing, Biomedical engineering, Microarrays, Molecular sequence and structure databases, Molecular dynamics and simulation, Molecular sequence classification, alignment and assembly, Image processing In medicine and biological sciences, Sequence analysis and alignment, Informatics and Statistics in Biopharmaceutical Research, Software tools for computational biology and bioinformatics, Comparative genomics; and more.
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 generalizatio...
Unsolved Problems of Noise and Fluctuations
All papers in this proceedings volume were peer reviewed. The purview of this third conference was shifted toward biology and medicine. Among the topics covered were: the constructive role of noise in the central nervous system, neuronal networks, and sensory transduction (hearing in humans, photo- and electroreception in marine animals), encoding of information into nerve pulse trains, single molecules and noise (including single molecule detection and characterization by nanopores - molecular "Coulter counting"), concepts of noise in neurophysiology (randomness and order in brain and heart electrical activities under normal conditions and in pathology), the role of noise in genetic regulation and gene expression, biosensors, etc.
The Second Tree
The Second Tree documents a biological revolution that will change the way you think about the material world, your own life and even the inevitability of your own death Genetic scientists are busily pushing back the boundaries of the humanly possible, climbing the branches of a tree of life that has been grafted by man, not God. Elaine Dewar chronicles the lives, the discoveries, and the feuds among modern biologists, exploring how they have crafted the tools to alter human evolution. She travels the globe on the trail of Charles Darwin and his intellectual descendants, telling the story of James D. Watson and his partner Francis Crick, who first described DNA; of Frederick Sanger, who inve...
