http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Front Cover; Predictive Modeling of Drug Sensitivity; Copyright; Contents; Preface; Chapter 1: Introduction; 1.1 Cancer Statistics; 1.2 Promise of Targeted Therapies; 1.3 Market Trends; 1.3.1 Biomarker Testing; 1.3.2 Pharmaceutical Solutions; 1.3.3 Value-Driven Outcomes; 1.4 Roadblocks to Success; 1.4.1 Linking Patient-Specific Traits to Efficacious Therapy; 1.4.2 High Costs of Targeted Therapies; 1.4.3 Resistance to Therapies; 1.4.4 Personalized Combination Therapy Clinical Trials; 1.5 Overview of Research Directions; References; Chapter 2: Data characterization; 2.1 Introduction.
2.2 Review of Molecular BiologyTranslation; Mutation; 2.3 Genomic Characterizations; 2.3.1 DNA Level; 2.3.2 Epigenetic Level; 2.3.3 Transcriptomic Level; 2.3.4 Proteome Level; 2.3.5 Metabolome Level; 2.3.6 Missing Value Estimation; 2.4 Pharmacology; 2.4.1 Pharmacokinetics; 2.4.2 Pharmacodynamics; 2.4.2.1 Modeling techniques; Indirect response models; 2.4.3 Software Packages; 2.4.4 Drug Toxicity; 2.5 Functional Characterizations; 2.5.1 Cell Viability Measurements; 2.5.2 Drug Characterizations; References; Chapter 3: Feature selection and extraction from heterogeneous genomic characterizations.
3.1 Introduction3.2 Data-Driven Feature Selection; 3.2.1 Filter Techniques; 3.2.1.1 Relief; Example to illustrate Relief; 3.2.1.2 Relief-F; 3.2.1.3 R-Relief-F; Example to illustrate regression ReliefF; 3.2.2 Wrapper Techniques; 3.2.2.1 Sequential forward search; 3.2.2.2 Sequential floating forward search; Example to illustrate SFFS; 3.3 Data-Driven Feature Extraction; 3.3.1 Principal Component Analysis; Example to illustrate PCA; 3.4 Multiomics Feature Extraction and Selection; 3.4.1 Category 1: Union of Transcriptomic and Proteomic Data.
3.4.2 Category 2: Extraction of Common Functional Context of Transcriptomic and Proteomic Features3.4.3 Category 3: Topological Network-Based Techniques; 3.4.4 Category 4: Missing Value Estimation of Proteomic Data Based on Nonlinear Optimization; 3.4.5 Category 5: Multiple Regression Analysis to Predict Contribution of Sequence Features in mRNA-Protein Correlation; 3.4.6 Category 6: Clustering-Based Techniques; 3.4.7 Category 7: Dynamic Modeling; References; Chapter 4: Validation methodologies; 4.1 Introduction; 4.1.1 Model Evaluation; 4.2 Fitness Measures; Data Representation.
4.2.1 Norm-Based Fitness Measures4.2.2 Correlation Coefficient; 4.2.3 Coefficient of Determination R2; 4.2.4 Akaike Information Criterion; 4.3 Sample Selection Techniques for Accuracy Estimation; 4.3.1 Resubstitution or Training Error; 4.3.2 Hold Out; 4.3.3 K-Fold Cross Validation; 4.3.4 Bootstrap; 4.3.5 Confidence Interval; 4.4 Small Sample Issues; 4.4.1 Simulation Study; 4.4.1.1 NCI-DREAM drug sensitivity dataset; 4.4.1.2 CCLE dataset; 4.4.1.3 Bias correction; 4.5 Experimental Validation Techniques; 4.5.1 In Vitro Cell Lines; 4.5.2 In Vitro Primary Tumor Cultures.
London, United Kingdom : Academic Press, [2016], ©2017