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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 6: gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data
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Chapter title
gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data
Chapter number 6
Book title
Data Mining for Biomedical Applications
Published by
Springer, Berlin, Heidelberg, April 2006
DOI 10.1007/11691730_6
Book ISBNs
978-3-54-033104-9, 978-3-54-033105-6
Authors

Haoliang Jiang, Shuigeng Zhou, Jihong Guan, Ying Zheng

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 5%
Greece 1 5%
Portugal 1 5%
Unknown 19 86%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 32%
Student > Ph. D. Student 7 32%
Professor > Associate Professor 3 14%
Researcher 2 9%
Professor 2 9%
Other 0 0%
Unknown 1 5%
Readers by discipline Count As %
Computer Science 11 50%
Mathematics 3 14%
Agricultural and Biological Sciences 2 9%
Biochemistry, Genetics and Molecular Biology 2 9%
Economics, Econometrics and Finance 1 5%
Other 1 5%
Unknown 2 9%