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Full-Text Articles in Biostatistics
Integrative Analysis Of Cancer Genomic Data, Shuangge Ma
Integrative Analysis Of Cancer Genomic Data, Shuangge Ma
Shuangge Ma
In the past decade, we have witnessed a period of unparallel development in the field of cancer genomics. To address the same or similar biomedical questions, multiple cancer genomic studies have been independently designed and conducted. Cancer gene signatures identified from analysis of individual datasets often have low reproducibility. A cost-effective way of improving reproducibility is to conduct integrative analysis of datasets from multiple studies with comparable designs. To properly integrate multiple studies and conduct integrative analysis, we need to access various public data warehouses, retrieve experiment protocols and raw data, evaluate individual studies and select those with comparable designs, …
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
A Tale Of Two Streets: Incorporating Grouping Structure In High Dimensional Data Mining, Shuangge Ma
A Tale Of Two Streets: Incorporating Grouping Structure In High Dimensional Data Mining, Shuangge Ma
Shuangge Ma
No abstract provided.
Identification Of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests, Yuanyuan Xiao, Mark Segal
Identification Of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests, Yuanyuan Xiao, Mark Segal
Mark R Segal
The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression …
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 …