Research
The Ma Lab is interested in creating novel methods of data analysis for use within the medical field. Our previous studies are shown below.
Research Software
- Software for Design and Analysis of Group Sequential Clinical Trials
- Software for Regularized ROC for Disease Classification and Biomarker Selection
- Software for Clustering Threshold Gradient Descent Regularization
- Software for Meta Threshold Gradient Descent Regularization
- Software for Combining clinical and genomic covariates via Cov-TGDR
- Cancer microarray analysis with clustering penalization
- Integrative analysis of prognosis data using 2-norm group bridge
- Expression Profiling
- Integrative Analysis
- Set Analysis
- Semiparametric Analysis
- Collaborations
Regularized Classification and Survival Analysis for Expression Profiling of Cancer
The objectives of this project are to develop novel statistical methods and computer packages for cancer classification and survival analysis using high-dimensional gene expression data and clinical measurements. The development of the proposed statistical methods that can deal with high-dimensional problems in estimating the relationship between cancer clinical outcomes and genomic data will contribute to better understanding of the genetic basis of cancer, better diagnoses, and better survival prediction, which in turn, can potentially have important impact on public health.
Study Period
January 1, 2008 - December 31, 2011
Acknowledgements
This study has been supported by RO1 CA120988 from NCI, NIH (P.I.: Dr. Jian Huang, Department of Statistics and Actuarial Science, University of Iowa). We would like to thank members of Yale Cancer Center and University of Iowa Holden Comprehensive Cancer Center for insightful discussions.