Pham Computational Biology

Issues and Applications in Oncology
1. Auflage 2009
ISBN: 978-1-4419-0811-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 310 Seiten

Reihe: Applied Bioinformatics and Biostatistics in Cancer Research

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.

Autoren/Hrsg.

Pham, Tuan

Inhaltsverzeichnis

1;Computational Biology;3
1.1;1 Identification of Relevant Genes from Microarray Experiments based on Partial Least Squares Weights: Application to Cancer Genomics;9
1.1.1;1.1 Introduction;9
1.1.2;1.2 Methods;11
1.1.2.1;1.2.1 Partial Least Squares Dimension Reduction;11
1.1.2.2;1.2.2 Variable Selection Measures as Functions of PLS Weights;11
1.1.2.3;1.2.3 Variable Influence Projection in Partial Least Squares;12
1.1.2.4;1.2.4 B-Partial Least Squares (B-PLS) Regression Coefficient;12
1.1.2.5;1.2.5 Random Augmentation VIP;13
1.1.3;1.3 Design of Simulation Studies;14
1.1.3.1;1.3.1 Simulation Based on Normal Model with Cluster-Specific Correlation;15
1.1.3.2;1.3.2 Resampling-Based Simulation from Real Data;16
1.1.4;1.4 Simulation Results;17
1.1.4.1;1.4.1 Result Based on Normal Model with Cluster-Specific Correlation;17
1.1.4.2;1.4.2 Resampling-Based Simulation Result;19
1.1.5;1.5 Applications to Microarray Gene Expression Data;20
1.1.6;1.6 Discussion;21
1.1.7;References;25
1.2;2 Geometric Biclustering and Its Applications to Cancer Tissue Classification Based on DNA Microarray Gene Expression Data;26
1.2.1;2.1 Introduction;26
1.2.2;2.2 Geometric Biclustering Patterns;29
1.2.2.1;2.2.1 Bicluster Types;29
1.2.2.2;2.2.2 Geometric Expressions of Biclusters;32
1.2.3;2.3 Geometric Biclustering Algorithms;33
1.2.3.1;2.3.1 Hough Transformation for Line Detection;34
1.2.3.1.1;2.3.1.1 The Classical Hough Transformation;34
1.2.3.1.2;2.3.1.2 Generalization of the Hough Transformation;35
1.2.3.2;2.3.2 Geometric Biclustering Algorithm;37
1.2.3.2.1;2.3.2.1 Additive and Multiplicative Pattern Plot;37
1.2.3.2.2;2.3.2.2 GBC Algorithm;38
1.2.3.2.3;2.3.2.3 Applications;40
1.2.3.3;2.3.3 Relaxation-Based Geometric Biclustering Algorithm;44
1.2.3.3.1;2.3.3.1 Nonlinear Probabilistic Relaxation Labeling;44
1.2.3.3.2;2.3.3.2 Algorithms;46
1.2.3.3.3;2.3.3.3 Applications;48
1.2.3.4;2.3.4 Geometric Biclustering Using Functional Modules (GBFM);51
1.2.3.4.1;2.3.4.1 Gene Annotation and Functional Modules;51
1.2.3.4.2;2.3.4.2 Algorithms;54
1.2.3.4.3;2.3.4.3 Applications;55
1.2.4;2.4 Conclusions;58
1.2.5;References;59
1.3;3 Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures;61
1.3.1;3.1 Introduction;61
1.3.1.1;3.1.1 Notation;62
1.3.2;3.2 Supervised Classification;62
1.3.2.1;3.2.1 Linear Classifier;63
1.3.2.2;3.2.2 Support Vector Machines;63
1.3.2.3;3.2.3 Nearest Centroid;64
1.3.2.4;3.2.4 Classification and Regression Trees;65
1.3.2.5;3.2.5 Error Rate Estimation;66
1.3.2.5.1;3.2.5.1 Apparent Error Rate;66
1.3.2.5.2;3.2.5.2 Cross-Validation;66
1.3.2.5.3;3.2.5.3 Bootstrap Approach;66
1.3.3;3.3 Variable Selection;67
1.3.3.1;3.3.1 Filter, Wrapper and Embedded Approaches;68
1.3.3.2;3.3.2 Recursive Feature Elimination;69
1.3.3.3;3.3.3 Nearest Shrunken Centroids;70
1.3.3.4;3.3.4 Random Forests;70
1.3.3.5;3.3.5 Extension to Multiclass;72
1.3.3.5.1;3.3.5.1 Division into Binary Problems;72
1.3.3.5.2;3.3.5.2 Unbalanced Multiclass Problems;72
1.3.3.6;3.3.6 Selection Bias and Performance Assessment;73
1.3.3.7;3.3.7 Optimal Size of the Selection;74
1.3.4;3.4 Illustrative Example with the Golub Data Set;74
1.3.4.1;3.4.1 Performance of the Three Feature Selection Methods;74
1.3.4.2;3.4.2 Comparison of the Gene Selections;76
1.3.4.3;3.4.3 Choice of Method;78
1.3.5;3.5 Validation;78
1.3.5.1;3.5.1 Biological Interpretation;78
1.3.5.2;3.5.2 Independent Test Set;79
1.3.6;3.6 Conclusion;79
1.3.7;References;80
1.4;4 Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling;83
1.4.1;4.1 Introduction;83
1.4.1.1;4.1.1 Biology of Breast Duct Epithelium;84
1.4.1.2;4.1.2 Pathobiology of DCIS;86
1.4.1.3;4.1.3 A Mini-Review of DCIS Modeling;87
1.4.1.4;4.1.4 Why Agent-Based Modeling?;89
1.4.2;4.2 Agent-Based Model of DCIS;90
1.4.2.1;4.2.1 A Brief Review of Exponential Random Variables and Poisson Processes;92
1.4.2.2;4.2.2 A Family of Potential Functions;93
1.4.2.3;4.2.3 Cell States;94
1.4.2.3.1;4.2.3.1 Quiescent Cells (Q);94
1.4.2.3.2;4.2.3.2 Proliferation (P);95
1.4.2.3.3;4.2.3.3 Apoptosis (A);96
1.4.2.3.4;4.2.3.4 Necrosis (N);97
1.4.2.3.5;4.2.3.5 Calcified Debris (C);98
1.4.2.4;4.2.4 Cell Motion Based upon the Balance of Forces;98
1.4.2.4.1;4.2.4.1 Cell–Cell Adhesion (Fcca);99
1.4.2.4.2;4.2.4.2 Cell–BM Adhesion (Fcba);99
1.4.2.4.3;4.2.4.3 (Calcified) Debris–(Calcified) Debris Adhesion (Fdda);100
1.4.2.4.4;4.2.4.4 Cell–Cell Repulsion (Including Calcified Debris) (Fccr);100
1.4.2.4.5;4.2.4.5 Cell–BM Repulsion (Including Debris) (Fcbr);100
1.4.2.5;4.2.5 Duct Geometry;101
1.4.2.6;4.2.6 Intraductal Oxygen Diffusion;101
1.4.3;4.3 Numerical Technique;102
1.4.3.1;4.3.1 Efficient Interaction Testing;103
1.4.4;4.4 Estimating Key Parameters;104
1.4.4.1;4.4.1 Cell Cycle and Apoptosis Time;104
1.4.4.2;4.4.2 Oxygen Parameters;105
1.4.4.3;4.4.3 Cell Mechanics;105
1.4.5;4.5 Application to Patient-Specific Modeling;105
1.4.5.1;4.5.1 Data Sources and Processing;106
1.4.5.2;4.5.2 Patient-Specific Calibration;106
1.4.5.3;4.5.3 Verification of Calibration;108
1.4.5.4;4.5.4 Sample Applications of the Calibrated Model;108
1.4.5.4.1;4.5.4.1 Parameter Study: Necrosis and Calcification Time;108
1.4.6;4.6 Ongoing and Future Work;112
1.4.7;References;113
1.5;5 Multicluster Class-Based Classification for the Diagnosis of Suspicious Areas in Digital Mammograms;118
1.5.1;5.1 Introduction;118
1.5.1.1;5.1.1 Background;118
1.5.1.2;5.1.2 Review of Existing Techniques;120
1.5.2;5.2 Research Methodology;122
1.5.2.1;5.2.1 Acquiring and Processing of Digital Mammograms;122
1.5.2.2;5.2.2 Creation of Multicluster Classes with Strong Clusters;123
1.5.2.3;5.2.3 Classification;124
1.5.2.3.1;5.2.3.1 Original Inputs with Multiple Classes;124
1.5.2.3.2;5.2.3.2 Cluster Values with Multiple Classes;125
1.5.3;5.3 Experimental Results and Comparative Analysis;125
1.5.4;5.4 Conclusions;127
1.5.5;References;127
1.6;6 Analysis of Cancer Data Using Evolutionary Computation;129
1.6.1;6.1 Introduction;129
1.6.2;6.2 Overview of Evolutionary Computation;133
1.6.2.1;6.2.1 Genetic Programming;133
1.6.2.1.1;6.2.1.1 Operations for Modifying the Tree;134
1.6.2.1.2;6.2.1.2 Control Parameters;135
1.6.2.2;6.2.2 Genetic Algorithms;135
1.6.2.3;6.2.3 Parallel Evolutionary Computation;136
1.6.2.3.1;6.2.3.1 Parallelism at Fitness Level;136
1.6.2.3.2;6.2.3.2 Parallelism at Population Level (Island Model or Cellular Model);137
1.6.2.3.3;6.2.3.3 Parameters of Island Model;137
1.6.2.3.4;6.2.3.4 Application Program Interface Tools;139
1.6.3;6.3 Analysis of Cancer Data;140
1.6.3.1;6.3.1 Genetic Programming for Binary Classification;140
1.6.3.1.1;6.3.1.1 Method;141
1.6.3.1.2;6.3.1.2 Experiments;142
1.6.3.2;6.3.2 Genetic Algorithms for Binary Classification;143
1.6.3.2.1;6.3.2.1 Concepts from Geometry;143
1.6.3.2.2;6.3.2.2 Nonlinear Programming Problem;144
1.6.3.2.3;6.3.2.3 Prediction;144
1.6.3.2.4;6.3.2.4 Solving Nonlinear Programming Problem by Genetic Algorithms;144
1.6.3.2.5;6.3.2.5 Experiments;145
1.6.3.3;6.3.3 Genetic Algorithm for Single-Class Classification;146
1.6.3.3.1;6.3.3.1 Nonlinear Programming Problem;147
1.6.3.3.2;6.3.3.2 Prediction;147
1.6.3.3.3;6.3.3.3 Using GA to Solve Nonlinear Programming Problem;147
1.6.3.3.4;6.3.3.4 Experiments;148
1.6.4;6.4 Conclusion;149
1.6.5;References;150
1.7;7 Analysis of Population-Based Genetic Association Studies Applied to Cancer Susceptibility and Prognosis;152
1.7.1;7.1 Genetic Variation and Its Implication in Cancer;152
1.7.2;7.2 Evolution of Genetic Epidemiology: From Family-Based to Population-Based Association Studies;154
1.7.3;7.3 Technical Issues and Data Quality Control for SNP-Array Association Studies;156
1.7.3.1;7.3.1 Introduction to Genotype Calling Algorithms;157
1.7.3.2;7.3.2 SNP-Level Quality Control;159
1.7.3.2.1;7.3.2.1 Percentage of Present Calls;159
1.7.3.2.2;7.3.2.2 Hardy–Weinberg Equilibrium;159
1.7.3.2.3;7.3.2.3 Minor Allele Frequency;160
1.7.3.2.4;7.3.2.4 Genotype Calling and Exploration of Signal Intensity Plots;161
1.7.3.3;7.3.3 Sample-Level Quality Control;162
1.7.3.3.1;7.3.3.1 Percentage of Present Calls;162
1.7.3.3.2;7.3.3.2 Sample Heterozygosity;162
1.7.3.3.3;7.3.3.3 Using Principal Components Analysis as a Method to Detect Outliers or Related Samples;163
1.7.4;7.4 Single-SNP Analysis: Association Between SNPs and a Trait;164
1.7.4.1;7.4.1 Binary Outcome;165
1.7.4.2;7.4.2 Quantitative Outcome;166
1.7.4.3;7.4.3 Prognosis Outcome;167
1.7.5;7.5 Multiple-SNP Analysis;167
1.7.5.1;7.5.1 Introduction to Haplotypes;168
1.7.5.2;7.5.2 Linkage Disequilibrium, Linkage Blocks, and Tag-SNPs;168
1.7.5.3;7.5.3 Haplotype Inference;170
1.7.5.4;7.5.4 Haplotype Association with Disease;170
1.7.6;7.6 Genome-Wide Association Studies;171
1.7.6.1;7.6.1 Study Designs;172
1.7.6.2;7.6.2 Assessing Association in GWAS;176
1.7.6.3;7.6.3 Statistical Power Calculations;177
1.7.6.4;7.6.4 Statistical Level Correction for Multiple Testing;178
1.7.7;7.7 Gene–Gene and Gene–Environment Interactions;182
1.7.8;7.8 Bioinformatics Tools and Databases for Genetic Association Studies;183
1.7.8.1;7.8.1 Genetic Association Suites;183
1.7.8.1.1;7.8.1.1 SNPStats;183
1.7.8.1.2;7.8.1.2 SNPassoc;184
1.7.8.1.3;7.8.1.3 PLINK;184
1.7.8.1.4;7.8.1.4 GAP;184
1.7.8.2;7.8.2 Haplotype-Only Software;185
1.7.8.2.1;7.8.2.1 Haploview;185
1.7.8.2.2;7.8.2.2 PHASE/fastPHASE;185
1.7.8.2.3;7.8.2.3 Haplo.stats;185
1.7.8.2.4;7.8.2.4 THESIAS;186
1.7.8.3;7.8.3 Web Databases;186
1.7.8.3.1;7.8.3.1 dbSNP;186
1.7.8.3.2;7.8.3.2 Hapmap;187
1.7.8.3.3;7.8.3.3 Genome Variation Server;187
1.7.8.4;7.8.4 Statistical Power Calculation;187
1.7.8.4.1;7.8.4.1 QUANTO;187
1.7.8.4.2;7.8.4.2 Genetic Power Calculator;188
1.7.8.4.3;7.8.4.3 CaTS;188
1.7.9;References;188
1.8;8 Selected Applications of Graph-Based Tracking Methods for Cancer Research;195
1.8.1;8.1 Introduction;195
1.8.2;8.2 Object Detection;196
1.8.3;8.3 Local Maximum and Blurring;197
1.8.4;8.4 Object Segmentation;197
1.8.5;8.5 Object Tracking;198
1.8.6;8.6 Algorithms on Graphs;198
1.8.7;8.7 Application to Lamellipodium Dynamics;200
1.8.8;8.8 Application to Mitotic Dynamics;202
1.8.9;8.9 Application to Cell Tracking;203
1.8.10;8.10 Conclusions and Perspectives;204
1.8.11;References;205
1.9;9 Recent Advances in Cell Classification for Cancer Research and Drug Discovery;206
1.9.1;9.1 Introduction;206
1.9.2;9.2 Nuclear Segmentation;209
1.9.2.1;9.2.1 Threshold-Based Segmentation;209
1.9.2.2;9.2.2 Image Thresholding;210
1.9.2.3;9.2.3 Fragment Merging Algorithm;210
1.9.3;9.3 Feature Extraction;211
1.9.3.1;9.3.1 Sequential Forward Selection;211
1.9.3.2;9.3.2 Automated Feature Weighting;212
1.9.3.3;9.3.3 Feature Scaling;212
1.9.4;9.4 Cell Phase Modeling;212
1.9.4.1;9.4.1 Feature Weighting-HMM;214
1.9.4.2;9.4.2 Feature Weighting-OMM;216
1.9.4.3;9.4.3 Feature Weighting-GMM;217
1.9.4.4;9.4.4 Feature Weighting-Fuzzy GMM;217
1.9.4.5;9.4.5 Feature Weighting-VQ;218
1.9.4.6;9.4.6 Feature Weighting-Fuzzy VQ;219
1.9.5;9.5 Algorithms for Modeling and Classifying Cell Phases;219
1.9.5.1;9.5.1 Modeling Algorithm;220
1.9.5.2;9.5.2 Classification Algorithm;220
1.9.6;9.6 Fuzzy Fusion of Classifiers;221
1.9.7;9.7 Experimental Results;223
1.9.7.1;9.7.1 Data Set;223
1.9.7.2;9.7.2 Feature Extraction;223
1.9.7.3;9.7.3 Initialization and Constraints on Parameters During Training;223
1.9.7.4;9.7.4 Experimental Results;224
1.9.8;9.8 Conclusion;225
1.9.9;References;225
1.10;10 Computational Tools and Resources for Systems Biology Approaches in Cancer;228
1.10.1;10.1 Introduction;228
1.10.2;10.2 Molecular Networks Involved in Cancer;229
1.10.2.1;10.2.1 Pathways Affected by Cancer Onset and Progression;229
1.10.2.2;10.2.2 Target Pathways of Cancer Treatment;229
1.10.3;10.3 Molecular Interaction Databases;231
1.10.3.1;10.3.1 BioCyc;232
1.10.3.2;10.3.2 KEGG;232
1.10.3.3;10.3.3 Reactome;232
1.10.3.4;10.3.4 ConsensusPathDB;233
1.10.3.5;10.3.5 TRANSPATH®;233
1.10.3.6;10.3.6 Annotation Tools;233
1.10.3.7;10.3.7 Modeling Tools;234
1.10.3.8;10.3.8 Systems Biology Workbench;235
1.10.3.9;10.3.9 JDesigner;235
1.10.3.10;10.3.10 CellDesigner;236
1.10.3.11;10.3.11 PyBioS;236
1.10.4;10.4 Computational Models for Cancer-Related Processes;236
1.10.4.1;10.4.1 BioModels Database;236
1.10.4.2;10.4.2 Specific Kinetic Models Relevant for Cancer;237
1.10.5;10.5 Discussion;239
1.10.6;References;240
1.11;11 Laser Speckle Imaging for Blood Flow Analysis;244
1.11.1;11.1 Introduction;245
1.11.2;11.2 Experimental Techniques and Setup;246
1.11.2.1;11.2.1 Experimental Setup;246
1.11.2.2;11.2.2 Laser Speckle Imaging;247
1.11.2.2.1;11.2.2.1 Effect of 2 2 or n n Hardware Binning on the Camera;248
1.11.2.2.2;11.2.2.2 Speckle Contrast, K;248
1.11.2.2.3;11.2.2.3 Decorrelation Time, c;248
1.11.2.2.4;11.2.2.4 Mean Flow Velocity, c;249
1.11.2.2.5;11.2.2.5 Parameter, N;250
1.11.2.3;11.2.3 Laser Speckle Contrast Analysis;250
1.11.2.4;11.2.4 Spatially Derived Contrast Using Temporal Frame Averaging;251
1.11.2.5;11.2.5 Temporally Derived Contrast;251
1.11.2.6;11.2.6 Modified Laser Speckle Imaging;252
1.11.3;11.3 Results and Discussion;252
1.11.3.1;11.3.1 Effects of Window Size M on KLASCA;253
1.11.3.2;11.3.2 Effects of n on KsLASCA;255
1.11.3.3;11.3.3 Effects of n on KtLASCA and NmLSI;255
1.11.3.4;11.3.4 Comparisons of KLASCA, KsLASCA, and KtLASCA;257
1.11.3.5;11.3.5 Evaluations on Visual Qualities;259
1.11.3.5.1;11.3.5.1 Subjective Quality;259
1.11.3.5.2;11.3.5.2 Objective Quality;265
1.11.3.6;11.3.6 Processing Time;267
1.11.3.6.1;11.3.6.1 LASCA;267
1.11.3.6.2;11.3.6.2 sLASCA, tLASCA, and mLSI with Varying M and n;267
1.11.4;11.4 Conclusions;269
1.11.5;References;270
1.12;12 The Challenges in Blood Proteomic Biomarker Discovery;273
1.12.1;12.1 Introduction;273
1.12.2;12.2 Blood Samples Preparation for Biomarker Discovery;275
1.12.2.1;12.2.1 Dynamical Range of Proteins;275
1.12.2.2;12.2.2 The Blood ``Peptidome';276
1.12.2.3;12.2.3 Other Biological Factors;277
1.12.3;12.3 Bioinformatics Algorithms in Biomarker Discovery;277
1.12.3.1;12.3.1 Baseline Removal;278
1.12.3.2;12.3.2 Denoising/Smoothing;282
1.12.3.2.1;12.3.2.1 Discrete Wavelet Transform;282
1.12.3.2.2;12.3.2.2 Matched Filtration;282
1.12.3.2.3;12.3.2.3 Savitzky–Golay and Average Moving;283
1.12.3.3;12.3.3 Normalization;283
1.12.3.3.1;12.3.3.1 Dividing by a Constant Value;284
1.12.3.3.2;12.3.3.2 Regression;284
1.12.3.3.3;12.3.3.3 Quantile;285
1.12.3.4;12.3.4 Peak Detection/Identification;285
1.12.3.5;12.3.5 Peak Alignment;287
1.12.3.6;12.3.6 Biomarker Candidate Identification;288
1.12.3.7;12.3.7 Clinical Diagnosis;289
1.12.3.8;12.3.8 Protein/Peptide Identification;290
1.12.4;12.4 Validation and Clinical Application;291
1.12.5;References;294
1.13;Index;300