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Articles 1 - 9 of 9
Full-Text Articles in Statistical Methodology
Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris
Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris
Jeffrey S. Morris
In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Jeffrey S. Morris
Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.
Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …
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
Health Insurance Coverage And Impact: A Survey In Three Cities In China, Shuangge Ma
Health Insurance Coverage And Impact: A Survey In Three Cities In China, Shuangge Ma
Shuangge Ma
No abstract provided.
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.
Health Insurance Coverage, Medical Expenditure And Coping Strategy: Evidence From Taiwan, Shuangge Ma
Health Insurance Coverage, Medical Expenditure And Coping Strategy: Evidence From Taiwan, Shuangge Ma
Shuangge Ma
No abstract provided.
Impact Of Illness And Medical Expenditure On Household Consumptions: A Survey In Western China, Shuangge Ma
Impact Of Illness And Medical Expenditure On Household Consumptions: A Survey In Western China, 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, …