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Data Mining for Biomedical Applications

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Cover of 'Data Mining for Biomedical Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions
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    Chapter 2 A Database Search Algorithm for Identification of Peptides with Multiple Charges Using Tandem Mass Spectrometry
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    Chapter 3 Filtering Bio-sequence Based on Sequence Descriptor
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    Chapter 4 Automatic Extraction of Genomic Glossary Triggered by Query
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    Chapter 5 Frequent Subsequence-Based Protein Localization
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    Chapter 6 gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data
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    Chapter 7 Automatic Orthologous-Protein-Clustering from Multiple Complete-Genomes by the Best Reciprocal BLAST Hits
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    Chapter 8 A Novel Clustering Method for Analysis of Gene Microarray Expression Data
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    Chapter 9 Heterogeneous Clustering Ensemble Method for Combining Different Cluster Results
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    Chapter 10 Rule Learning for Disease-Specific Biomarker Discovery from Clinical Proteomic Mass Spectra
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    Chapter 11 Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles
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    Chapter 12 Generation of Comprehensible Hypotheses from Gene Expression Data
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    Chapter 13 Classification of Brain Glioma by Using SVMs Bagging with Feature Selection
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    Chapter 14 Missing Value Imputation Framework for Microarray Significant Gene Selection and Class Prediction
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    Chapter 15 Informative MicroRNA Expression Patterns for Cancer Classification
Attention for Chapter 5: Frequent Subsequence-Based Protein Localization
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Chapter title
Frequent Subsequence-Based Protein Localization
Chapter number 5
Book title
Data Mining for Biomedical Applications
Published by
Springer, Berlin, Heidelberg, April 2006
DOI 10.1007/11691730_5
Book ISBNs
978-3-54-033104-9, 978-3-54-033105-6
Authors

Osmar R. Zaïane, Yang Wang, Randy Goebel, Gregory Taylor

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Researcher 1 25%
Unknown 2 50%
Readers by discipline Count As %
Computer Science 1 25%
Engineering 1 25%
Unknown 2 50%