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- Regularized Analysis of Large p, Small n Data (5)
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- General Biostatistics (1)
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- Integrative analysis; Contrasted penalization; Marker selection; High-throughput cancer studies. (1)
- Marker identification (1)
- Model sparsity structure (1)
- Pathway analysis; Breast cancer prognosis; Regularized estimation. (1)
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Articles 1 - 29 of 29
Full-Text Articles in Physical Sciences and Mathematics
Promoting Similarity Of Model Sparsity Structures In Integrative Analysis Of Cancer Genetic Data, Shuangge Ma
Promoting Similarity Of Model Sparsity Structures In Integrative Analysis Of Cancer Genetic Data, Shuangge Ma
Shuangge Ma
In profiling studies, the analysis of a single dataset often leads to unsatisfactory results because of the small sample size. Multi-dataset analysis utilizes information across multiple independent datasets and outperforms single-dataset analysis. Among the available multi-dataset analysis methods, integrative analysis methods aggregate and analyze raw data and outperform meta-analysis methods, which analyze multiple datasets separately and then pool summary statistics. In this study, we conduct integrative analysis and marker selection under the heterogeneity structure, which allows different datasets to have overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect some similarity of identified …
Measures For The Degree Of Overlap Of Gene Signatures And Applications To Tcga, Shuangge Ma
Measures For The Degree Of Overlap Of Gene Signatures And Applications To Tcga, Shuangge Ma
Shuangge Ma
For cancer and many other complex diseases, a large number of gene signatures have been generated. In this study, we use cancer as an example and note that other diseases can be analyzed in a similar manner. For signatures generated in multiple studies on the same cancer type/outcome, and for signatures on different cancer types, it is of interest to evaluate their degree of overlap. Many of the existing studies simply count the number (or percentage) of overlapped genes shared by two signatures. Such an approach has serious limitations. In this study, as a demonstrating example, we consider cancer prognosis …
Integrative Analysis Of High-Throughput Cancer Studies With Contrasted Penalization, Shuangge Ma
Integrative Analysis Of High-Throughput Cancer Studies With Contrasted Penalization, Shuangge Ma
Shuangge Ma
In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms ``classic" meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by …
Integrative Analysis Of Prognosis Data On Multiple Cancer Subtypes, Shuangge Ma
Integrative Analysis Of Prognosis Data On Multiple Cancer Subtypes, Shuangge Ma
Shuangge Ma
In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe …
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Shuangge Ma
No abstract provided.
Lecture 5, Shuangge Ma
Final Project, Shuangge Ma
Lecture 4, Shuangge Ma
Lecture 4, Shuangge Ma
Computer Intensive Methods Lecture 13, Shuangge Ma
Final Project (Description), Shuangge Ma
Final Project (Data), Shuangge Ma
Lecture 3, Shuangge Ma
Lecture 2, Shuangge Ma
Reference: Multiple Imputation, Shuangge Ma
Reference: Weighted Bootstrap, Shuangge Ma
Computer Intensive Methods Lecture 9, Shuangge Ma
Computer Intensive Methods Lecture 8, Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 6, Shuangge Ma
Reference: Block Jackknife, Shuangge Ma
Computer Intensive Methods Lecture 5, Shuangge Ma
Reading: Simulate Multivariate Distribution, Shuangge Ma
Reading: Simulate Multivariate Distribution, Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 4, Shuangge Ma
Computer Intensive Methods Lecture 3 (Lab 1), Shuangge Ma
Computer Intensive Methods Lecture 3 (Lab 1), Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 2, Shuangge Ma
Computer Intensive Methods Lecture 1, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Shuangge Ma
Prognosis of breast cancer is of great scientific and practical interest. Biomedical studies suggest that clinical and environmental risk factors do not have satisfactory predictive power for prognosis. Multiple gene profiling studies have been conducted, searching for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with similar biological functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Although multiple pathway analysis methods are available, they have certain drawbacks and are not suitable for the proposed analysis. In this article, we develop a new …