Data Mining for Genomics and Proteomics (eBook)

Darius M. Dziuda, PhD, is Associate Professor of Data Mining and Statistics in the Department of Mathematical Sciences at Central Connecticut State University (CCSU). His research and professional activities have been focused on efficient data mining of biomedical data and on methods for identification of parsimonious multivariate biomarkers for medical diagnosis, prognosis, personalized medicine, and drug discovery. For CCSU's data mining program, Dr. Dziuda developed and teaches graduate-level courses on Data Mining for Genomics and Proteomics and on Biomarker Discovery.

1. Introduction.

1.1 Basic terminology.

1.2 Overlapping areas of research.

1.2.1 Genomics.

1.2.2 Proteomics.

1.2.3 Bioinformatics.

1.2.4 Transcriptomics and other - omics ....

1.2.5 Data mining.

1. Basic analysis of gene expression microarray data.

2.1 Introduction.

2.2 Microarray technology.

2.3 Low-level preprocessing of Affymetrix microarrays.

2.4 Public repositories of microarray data.

2.5 Gene expression matrix.

2.6 Additional preprocessing, quality assessment and filtering.

2.7 Basic exploratory data analysis.

2.8 Unsupervised learning (taxonomy-related analysis).

2.8.1 Cluster analysis.

2.8.2 Principal component analysis.

2.8.3 Self-organizing maps.

2.9 Exercises.

1. Biomarker Discovery and Classification.

3.1 Overview.

3.2 Feature Selection.

3.2.1 Introduction.

3.2.2 Univariate versus multivariate approaches.

3.2.3 Supervised versus unsupervised methods.

3.2.4 Taxonomy of feature selection methods.

3.2.5 Feature selection for multiclass discrimination.

3.2.6 Regularization and feature selection.

3.2.7 Stability of biomarkers.

3.3 Discriminant Analysis.

3.3.1 Introduction.

3.3.2 Learning Algorithm.

3.3.3 A stepwise hybrid feature selection with T2.

3.4 Support Vector Machines.

3.4.1 Hard-Margin Support Vector Machines.

3.4.2 Soft- Margin Support Vector Machines.

3.4.3 Kernels.

3.4.4 SVMs and multiclass discrimination.

3.4.5 SVMs and Feature Selection: Recursive Feature Elimination.

3.4.6 Summary.

3.5 Random Forests.

3.5.1 Introduction.

3.5.2 Random Forests Learning Algorithm.

3.5.3 Random Forests and Feature Selection.

3.5.5 Summary.

3.6 Ensemble classifiers, bootstrap methods, and the modified bagging schema.

3.6.1 Ensemble classifiers.

3.6.2 Bootstrap methods.

3.6.3 Bootstrap and linear discriminant analysis.

3.6.4 The modified bagging schema.

3.7 Other learning algorithms.

3.7.1 k-Nearest Neighbor classifiers.

3.7.2 Artificial Neural Networks.

3.8 Eight commandments of gene expression analysis (for biomarker discovery).

3.9 Exercises.

1. The Informative Set of Genes.

4.1 Introduction.

4.2 Definitions.

4.3 The method.

4.3.1 Identification of the Informative Set of Genes.

4.3.2 Primary expression patterns of the Informative Set of Genes.

4.3.3 The most frequently used genes of the primary expression patterns.

4.4 Using the Informative Set of Genes to identify robust multivariate biomarkers.

4.5 Summary.

4.6 Exercises.

1. Analysis of protein expression data.

5.1 Introduction.

5.2 Protein chip technology.

5.2.1 Antibody microarrays.

5.2.2 Peptide microarrays.

5.2.3 Protein microarrays.

5.2.4 Reverse phase microarrays.

5.3 Two-dimensional gel electrophoresis.

5.4 MALDI-TOF and SELDI-TOF mass spectrometry.

5.5 Preprocessing of mass spectrometry data.

5.6 Analysis of protein expression data.

5.6.1 Additional preprocessing.

5.6.2 Basic exploratory data analysis.

5.6.3 Unsupervised learning.

5.6.4 Supervised learning - feature selection and biomarker discovery.

5.6.5 Supervised learning - classification systems.

5.7 Associating biomarker peaks with proteins.

5.7.1 Introduction.

5.7.2 The Universal Protein Resource (UniProt).

5.7.3 Search programs.

5.7.4 Tandem mass spectrometry.

5.8 Summary.

1. Sketches for selected exercises.

6.1 Introduction.

6.2 Multiclass discrimination (Exercise 3.2).

6.3 Identifying the Informative Set of Genes (Exercises 4.2 to 4.6).

6.4 Using the Informative set of Genes to identify robust multivariate markers (Exercise 4.8).

6.5 Validating biomarkers on an independent test data set (Exercise 4.8).

6.6 Using a training set that combines more than one data set (Exercises 3.5 and 4.1 to 4.8).