Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Biostatistics
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 …
Incorporating Network Structure In Integrative Analysis Of Cancer Prognosis Data, Shuangge Ma
Incorporating Network Structure In Integrative Analysis Of Cancer Prognosis Data, Shuangge Ma
Shuangge Ma
In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly-connected nodes (genes) are more likely to have related biological functions and similar regression …
Risk Factors Of Follicular Lymphoma, Shuangge Ma
Integrative Analysis Of Multiple Cancer Genomic Datasets Under The Heterogeneity Model, Shuangge Ma
Integrative Analysis Of Multiple Cancer Genomic Datasets Under The Heterogeneity Model, Shuangge Ma
Shuangge Ma
No abstract provided.
Identification Of Gene-Environment Interactions In Cancer Prognosis Studies Using Penalization, Shuangge Ma
Identification Of Gene-Environment Interactions In Cancer Prognosis Studies Using Penalization, Shuangge Ma
Shuangge Ma
High-throughput cancer studies have been extensively conducted, searching for genetic risk factors independently associated with prognosis beyond clinical and environmental risk factors. Many studies have shown that the gene-environment interactions may have important implications. Some of the existing methods, such as the commonly adopted single-marker analysis, may be limited in that they cannot accommodate the joint effects of a large number of genetic markers or use ineffective marker identification techniques. In this study, we analyze cancer prognosis studies, and adopt the AFT (accelerated failure time) model to describe survival. A weighted least squares approach, which has the lowest computational cost, …