Screen LibraryMachine learning in bioinformatics
Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc., have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This book compiles...
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| Corporate Authors: | |
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| Group Author: | ; |
| Published: |
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| Literature type: | Electronic eBook |
| Language: | English |
| Series: |
Wiley series on bioinformatics
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| Subjects: | |
| Online Access: |
http://onlinelibrary.wiley.com/book/10.1002/9780470397428 |
| Summary: |
Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc., have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This book compiles recent approaches in machine learning, showing promise in addressing different complex bioinformatics applications from prominent researchers in the field. |
| Carrier Form: | 1 online resource (xviii, 456 p.) : ill. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780470397428 047039742X 0470397411 (electronic bk.) 9780470397411 (electronic bk.) |
| Index Number: | QH324 |
| CLC: | Q811.4 |
| Contents: |
Feature selection for genomic and proteomic data mining / Comparing and visualizing gene selection and classification methods for microarray data / Adaptive kernel classifiers via matrix decomposition updating for biological data analysis / Bootstrapping consistency method for optimal gene selection from microarray gene expression data for classification problems / Fuzzy gene mining : a fuzzy-based framework for cancer microarray data analysis / |