Big Data Analytics in Biostatistics and Bioinformatics
portes grátis
Big Data Analytics in Biostatistics and Bioinformatics
Zhao, Yichuan; Chen, Ding-Geng
Springer Nature Switzerland AG
04/2026
489
Dura
Inglês
9783032066480
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Part I: An Overview of Big Data Analytics in Biostatistics and Bioinformatics.- Chapter 1 Big Data Analytics in Biostatistics and Bioinformatics: The Past, The Present and The Future.- Chapter 2 Navigating Sample Size Dilemmas in ML-based Predictive Analytics: A Comprehensive Review.- Chapter 3 Moving Beyond Mean: Harnessing Big Data for Health Insights by Quantile Regression.- Chapter 4. Incorrect Model Selection Using R2 and Akaike Information Criterion in Big Data Analyses.- Chapter 5 False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies.- Part II: Statistical Methods of Bayesian Analysis and Gene Expression Data.- Chapter 6 Investigating and Assessing Diverse Strategies and Classification Techniques Applied in the Integration of Multi-Omics Data.- Chapter 7 Sparse Bayesian Clustering of Matrix Data.- Chapter 8 Bayesian Kernel Based Modeling and Selection of Genetic Pathways and Genes in Cancer Studies: A Step Toward Targeted Treatment Protocols.- Chapter 9 Using Guided Regularized Random Forests to Identify Important Biological Pathways and Genes.- Chapter 10 Ultrahigh-Dimensional Discriminant Analysis and Its Application to Gene Expression Data.- Part III: Deep Learning and Neural Network.- Chapter 11 Deep Image-on-scalar Regression Model with Hidden Confounders.- Chapter 12 Transfer Learning for Causal Effect Estimation.- Chapter 13 Hybrid Distance for Classification of Complex Biological Data Based on Elastic Shape Analysis of Curves and Topological Data Analysis of Point Clouds.- Chapter 14 Bifurcation Analysis of an Analog Hopfield Neural Network with Three Time Delays.- Chapter 15 Advancing Information Integration through Empirical Likelihood: Selective Reviews and a New Idea.- Part IV: Clinical Trials and Survival Analysis.- Chapter 16 Hierarchical Semi-parametric Bayesian Modeling in Patient Screening and Enrollment Dynamic Prediction for Multicenter Clinical Trials.- Chapter 17 Comparative Effectiveness Analysis of Lobectomy and Limited Resection for Elderly Non-Small Cell Lung Cancer Patients via Emulation.- Chapter 18 Recent Developments in Joint Modeling for Recurrent Gap Times with a Terminal Event.- Chapter 19 A Conditional Modelling Approach for Dynamic Risk Prediction of a Survival Outcome Using Longitudinal Biomarkers with an Application to Ovarian Cancer.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
multiple testing;false discovery rate;transfer Learning;deep Image-on-scalar;causal effect estimation;subset selection;variable selection;hierarchical model;randomized trials;lasso;biological pathways;data integration;classification;feature screening;graphical models;graphical models;classification;hidden confounder;limit of detection;adaptive design
Part I: An Overview of Big Data Analytics in Biostatistics and Bioinformatics.- Chapter 1 Big Data Analytics in Biostatistics and Bioinformatics: The Past, The Present and The Future.- Chapter 2 Navigating Sample Size Dilemmas in ML-based Predictive Analytics: A Comprehensive Review.- Chapter 3 Moving Beyond Mean: Harnessing Big Data for Health Insights by Quantile Regression.- Chapter 4. Incorrect Model Selection Using R2 and Akaike Information Criterion in Big Data Analyses.- Chapter 5 False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies.- Part II: Statistical Methods of Bayesian Analysis and Gene Expression Data.- Chapter 6 Investigating and Assessing Diverse Strategies and Classification Techniques Applied in the Integration of Multi-Omics Data.- Chapter 7 Sparse Bayesian Clustering of Matrix Data.- Chapter 8 Bayesian Kernel Based Modeling and Selection of Genetic Pathways and Genes in Cancer Studies: A Step Toward Targeted Treatment Protocols.- Chapter 9 Using Guided Regularized Random Forests to Identify Important Biological Pathways and Genes.- Chapter 10 Ultrahigh-Dimensional Discriminant Analysis and Its Application to Gene Expression Data.- Part III: Deep Learning and Neural Network.- Chapter 11 Deep Image-on-scalar Regression Model with Hidden Confounders.- Chapter 12 Transfer Learning for Causal Effect Estimation.- Chapter 13 Hybrid Distance for Classification of Complex Biological Data Based on Elastic Shape Analysis of Curves and Topological Data Analysis of Point Clouds.- Chapter 14 Bifurcation Analysis of an Analog Hopfield Neural Network with Three Time Delays.- Chapter 15 Advancing Information Integration through Empirical Likelihood: Selective Reviews and a New Idea.- Part IV: Clinical Trials and Survival Analysis.- Chapter 16 Hierarchical Semi-parametric Bayesian Modeling in Patient Screening and Enrollment Dynamic Prediction for Multicenter Clinical Trials.- Chapter 17 Comparative Effectiveness Analysis of Lobectomy and Limited Resection for Elderly Non-Small Cell Lung Cancer Patients via Emulation.- Chapter 18 Recent Developments in Joint Modeling for Recurrent Gap Times with a Terminal Event.- Chapter 19 A Conditional Modelling Approach for Dynamic Risk Prediction of a Survival Outcome Using Longitudinal Biomarkers with an Application to Ovarian Cancer.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
multiple testing;false discovery rate;transfer Learning;deep Image-on-scalar;causal effect estimation;subset selection;variable selection;hierarchical model;randomized trials;lasso;biological pathways;data integration;classification;feature screening;graphical models;graphical models;classification;hidden confounder;limit of detection;adaptive design
