Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

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Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) Overview

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

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• Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
• Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins
• An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Pattern Recognition and Machine Learning (Information Science and Statistics)

Computational Systems Bioinformatics -- Methods And Biomedical Applications

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Computational Systems Bioinformatics -- Methods And Biomedical Applications Overview

Computational systems biology is a new and rapidly developing field of research, concerned with understanding the structure and processes of biological systems at the molecular, cellular, tissue, and organ levels through computational modeling as well as novel information theoretic data and image analysis methods. By focusing on either information processing of biological data or on modeling physical and chemical processes of biosystems, and in combination with the recent breakthrough in deciphering the human genome, computational systems biology is guaranteed to play a central role in disease prediction and preventive medicine, gene technology and pharmaceuticals, and other biotechnology fields.

This book begins by introducing the basic mathematical, statistical, and data mining principles of computational systems biology, and then presents bioinformatics technology in microarray and sequence analysis step-by-step. Offering an insightful look into the effectiveness of the systems approach in computational biology, it focuses on recurrent themes in bioinformatics, biomedical applications, and future directions for research.

Contents: Introduction; Mathematical, Statistical, and Data Mining Principles; Missing Value Estimation Using KNN and Bayesian Methods; Normalization, Scaling, and Discretization; Cross-Platform Comparison; Discovering Differential Biomarkers; Gene Selection and Classification; Gene Clustering; Gene Prediction; Reverse Engineering: Gene Regulatory Network Construction and Its Behavior; Motif Detection and Transcription Factor Binding Site (TFBS) Identification; Integrated Microarray and Gene Sequence Analysis; Protein Structure Informatics; Biomarker Discovery from Proteomics; Bioimaging Informatics; Conclusions.

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