Big Data Analytics in Oncology with R
- Length: 254 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2022-12-29
- ISBN-10: 1032028769
- ISBN-13: 9781032028767
- Sales Rank: #0 (See Top 100 Books)
Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area.
Features:
Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
Cover Half Title Title Page Copyright Page Dedication Contents Preface Author 1. Survival Analysis 1.1. Introduction 1.2. Hazard Function 1.3. Censoring 1.4. Study Design and Survival Analysis 1.5. Survival Analysis Objective 1.6. Non-Parametric Approach for Survival Analysis 1.7. Log-Rank Test 1.8. Median Follow-Up Time Calculation 1.9. Survival Data 1.9.1. Multiple event-time data 1.9.2. Multivariate survival data 1.9.3. Univariate survival models 1.9.4. Multivariate survival models 1.9.5. Doubly interval-censored survival data 1.9.6. Frequentist approach 1.10. Bayesian Prior Assumptions for Survival Analysis 1.10.1. Prior in survival analysis 1.10.2. Dirichlet process prior 1.11. Illustration Using R 2. Cox Proportional Survival Analysis 2.1. Introduction 2.2. Cox Proportional Hazard 2.2.1. Hazard ratio 2.2.2. Partial likelihood function 2.2.3. Wald score and Likelihood ratio tests 2.3. Cox Proportional Diagnostics Test 2.3.1. Cox-snell residual 2.3.2. Martingale residual 2.4. Mean and Median Survival Time 2.5. Stratified Cox Proportional Hazard Test 2.6. Schoenfeld Residuals 2.7. Extended Cox Regression Model 2.8. Illustration Using R 2.8.1. Univariate Cox proportional hazard in high dimensional data 2.8.2. Expectation-maximization algorithm 3. Parametric Survival Analysis 3.1. Introduction 3.2. Regularized Survival Analysis 3.3. Gaussian Prior and Ridge Regression 3.4. Laplacian Prior and Lasso Regression 3.5. Parameteric Survival Analysis 3.6. Different Distribution 3.6.1. Exponential distribution 3.6.2. Weibull model 3.6.3. Gamma distribution 3.7. Maximum Likelihood Estimation 3.8. Illustration Using R 4. Competing Risk Modeling in High Dimensional Data 4.1. Introduction 4.2. Survival and Competing Risk Model 4.3. The Competing Risk Models 4.4. Aalen's Additive Hazards Model 4.5. Bayesian Formulation 4.6. The Lasso Method 4.7. Metropolis Algorithm 4.8. Deviance Information Criterion and Akaike Information Criteria 4.9. Illustration with Example Data 4.10. Bayesian for Competing Risk Analysis Illustration Using R 5. Biomarker Thresholding in High Dimensional Data 5.1. Introduction 5.2. Statistical Methodology for Biomarker Thresholding 5.3. Thresholding for Repeatedly Measured Biomarker 5.4. Statistical Model 5.5. Repeteadly Measured Biomarker Thresholding 5.6. Biomarkar Thresholding Determination 5.7. Illustration Using R 5.8. Data Illustration 5.9. Classification and Regression Tree Analysis in Biomarker Thresholding 6. High Dimensional Survival Data Analysis 6.1. Introduction 6.2. Challenges in High Dimensional Data 6.3. Variable Selection in High Dimensional Data 6.3.1. Lasso selection 6.3.2. Elastic net 6.3.3. Cox regression 6.4. Survival and High Dimensional Data 6.5. Covariance Structure in High Dimensional Data 6.6. Variable Selection 6.6.1. Bayesian information criterion 6.6.2. Deviance information criterion 6.6.3. Predictive criteria 6.7. Illustration Using R 6.7.1. Data flietration with batches 7. Frailty Models 7.1. Introduction 7.2. Proportional Hazard Model 7.2.1. Single event frailty model 7.2.2. Clustered wise frailty 7.2.3. Recurrent events 7.3. Frailty Model 7.3.1. Frailty distribution 7.3.2. Univariate frailty model 7.3.3. Correlated frailty model 7.3.4. Clustered survival data 7.3.5. Covariates 7.4. Illustration 7.4.1. Diabetic retinopathy study 7.4.2. Canadian health and aging study 7.5. Frailty Model in Packages 7.6. Frailty and Biomarker 7.7. Illustration Using R 8. Time-Course Gene Expression Data Analysis 8.1. Introduction 8.2. Microarray Data 8.2.1. Source of microarray data 8.2.2. Gene expression and microarray data 8.3. Model for Microarray Data 8.3.1. Bayesian state space modeling 8.4. Different Covariance Structure 8.4.1. Variance Components (VC) covariance structure 8.4.2. First order Auto Regressive AR(1) 8.4.3. Unstructured (US) 8.5. Model Development 8.5.1. Gene selection procedure 8.5.2. Model fitting and prediction 8.5.3. Parameter estimation 8.5.4. Prediction of gene expression 8.5.5. Study design 8.5.6. Longitudinal over cross sectional gene expression 8.5.7. Short time course experiment 8.5.8. Replication 8.5.9. Identifying the genes of interest 8.5.10. ANOVA and F-statistic 8.5.11. Moderation 8.5.12. Gene-specific moderation 8.6. Likelihood-Based Approach 8.7. Empirical Bayes Approach 8.8. Illustration Using R 9. Survival Analysis and Time-course Data Analysis 9.1. Introduction 9.1.1. Cox proportional hazard model and filtration 9.1.2. Multivariate joint model 9.1.2.1. The mixed model 9.1.2.2. The Cox model 9.1.2.3. The Joint model 9.1.3. Bayesian approach in joint longitudinal and survival modeling 9.1.4. Description of data 9.2. Model Fitting 9.3. Results 9.3.1. The linear mixed effect model 9.3.2. The Cox model 9.3.3. The joint longitudinal and survival model 9.3.4. Model validation 9.4. Discussion 10. Features Selection in High Dimensional Time to Event Data 10.1. Introduction 10.2. Different Methods in Feature Selection 10.2.1. Filter method 10.2.2. Wrapper method 10.2.3. Embedded method 10.2.4. Other methods 10.2.5. Limitations of existing methods 10.2.6. Re-sampling algorithm 10.3. Distribution of Weight in Feature Selection 10.3.1. Re-sampling feature selection steps 10.4. Data Methodology 10.5. Weight Function and The Re-sampling Algorithm 10.6. High Dimensional Time to event 10.6.1. Time to event data 10.6.2. Gene expression data 10.6.3. Machine learning algorithms 10.6.4. Machine learning codes with high dimensional data 10.7. Methodological Framework 10.7.1. Feature selection 10.7.2. Frailty analysis 10.7.3. Classification using CPH model in time-course data 10.7.4. Sequential threshold selection 10.8. llustration Using R 10.8.1. Implementation details 10.8.1.1. Feature selection using CPH learner model 10.8.1.2. Feature selection using kaplan method learner model 10.8.1.3. Fraity analysis with high dimensional data 10.8.1.4. Sequential thresholding of correlated biomarkers 10.8.1.5. Gene classification using longitudinal gene expressions 10.8.1.6. mlclassKap 10.9. Discussion Bibliography Index
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.