Bioinformatics. The Machine Learning Approach.pdf

Bioinformatics. The Machine Learning Approach

Soren Brunak

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 more than ever. 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 in which there is a lot of data but little theory, as in molecular biology. The goal in machine learning is to extract useful information from a body by building good probabilistic models - and to automate the process as much as possible.Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. This 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 edition contains expanded coverage of probabilistic graphical models and the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Deep learning-based clustering approaches for … Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only

6.51 MB Taille du fichier
9780262025065 ISBN
Bioinformatics. The Machine Learning Approach.pdf

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Sofya Voigtuh

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

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Mattio Müllers

Deep Learning Applications in Bioinformatics. Bioinformatics, or computational biology, is the science of interpreting biological data through computer science. Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning series) eBook: Baldi, Pierre, Brunak, Søren: Amazon.in: Kindle  ...

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Noels Schulzen

Bioinformatics: The Machine Learning Approach …

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Jason Leghmann

Bioinformatics: The Machine Learning Approach - …

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Jessica Kolhmann

Bioinformatics The Machine Learning Approach.pdf … Bioinformatics The Machine Learning Approach.pdf - Free download Ebook, Handbook, Textbook, User Guide PDF files on the internet quickly and easily.