Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
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Summary
Recent development in statistics for integrative analyses of cancer data is reviewed, including meta-analysis of homogeneous type of data across multiple studies, integrating multiple heterogeneous genomic data types, survival analysis with high- or ultrahigh-dimensional genomic profiles, and cross-data-type prediction where both predictors and responses are high-or ultra high-dimensional vectors.
- Type
- review
- Published
- 2015-01-01
- Cited by
- 19
- References
- 94
- Access
- Open access
- OpenAlex
- https://openalex.org/W1675799914
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2905351
Keywords
Data science, Data type, Epigenomics, Genomics, Scope (computer science)
References
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- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Cancer Informatics
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- Independent screening for single‐index hazard rate models with ultrahigh dimensional features
- Estimating time-varying networks
- SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS
- Survival analysis with high-dimensional covariates
- Similarity network fusion for aggregating data types on a genomic scale
- A decision-theoretic generalization of on-line learning and an application to boosting
- Addendum: Regularization and variable selection via the elastic net
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Structural pursuit over multiple undirected graphs
- Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
- Pattern discovery and cancer gene identification in integrated cancer genomic data
- A general framework for multiple testing dependence
Cited by
- Integrating heterogeneous genomic data to accurately identify disease subtypes
- Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence
- Identifying MicroRNA and Gene Expression Networks Using Graph Communities
- Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods
- Bi-level and Bi-objective p-Median Type Problems for Integrative Clustering: Application to Analysis of Cancer Gene-Expression and Drug-Response Data
- The p-median Problem with Order for Two-Source Clustering
- Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer
- A Nonparametric Bayesian Method for Clustering of High-Dimensional Mixed Dataset.
- Evaluation of integrative clustering methods for the analysis of multi-omics data
- Integration of Omics Data to Identify Cancer-Related MicroRNA.
- Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival
- An integrated transcriptomics and metabolomics study of the immune response of newly hatched chicks to the cytosine-phosphate-guanine oligonucleotide stimulation
- Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions
- Integrative and sparse singular value decomposition method for biclustering analysis in multi-sources dataset
- Identifying Cancer Patient Subgroups by Finding Co-Modules From the Driver Mutation Profiles and Downstream Gene Expression Profiles
- How does cloud computing improve cancer information management? A systematic review
- Integrative Classification Using Structural Equation Modeling of Homeostasis
- Research Considerations in Patients with Cancer and Comorbidity
- Integrating Open Data on Cancer in Support to Tumor Growth Analysis
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