Integrative analysis of genomic data and marker identification

• Acknowledgements: This study has been supported by
  • R03LM009828 from NLM/NIH (National Library of Medicine): Efficient microarray meta analysis and cancer biomarker selection. Funding period: 09/01/2009--08/31/2010. PI: Shuangge Ma.
  • CTSA award to Yale Center for Clinical investigation: Identifying common transcriptional profiles of neoplastic tranformation and progression via integrative microarray analysis. Funding period: 1/1/2008--06/30/2010. PI: R. Sherwin. Project PI: Shuangge Ma.
  
  
• Goal: The goal of this study is to develop novel meta analysis and integrative analysis methods that can pool and analyze data from multiple heterogeneous genomic studies and select important markers. This study can provide more efficient usage of existing genomic data, provide more accurate predictions and more insights into the genomic mechanisms of disease occurrence and progression, and in general benefit pharmacogenomic studies.
  
  
• Publications:
  1. Ma, S. and Huang, J. (2009) Regularized gene selection in cancer microarray meta-analysis. BMC Bioinformatics, 10:1.
  2. Ma, S., Huang, J. and Moran, M. (2009) Identification of genes associated with multiple cancers via integrative analysis. BMC Genomics, 10: 535.
  3. Ma, S. and Kosorok, M.R. (2010) Detection of gene pathways with predictive power for breast cancer prognosis. BMC Bioinformatics, 11:1.
  
  
• Software:Software for Meta Threshold Gradient Descent Regularization
  
  
• Presentations:
  1. Integrative analysis of cancer genomic data. The 57th Session of the International Statistical Institute. August 18th, 2009.
  2. Identification of cancer-associated gene pathways from analysis of expression data. JSM. August 3rd, 2009.
  3. A Tale of Two Streets: Incorporating grouping structure in high dimensional data mining. Data Mining and Business Intelligence Conference. June 6th, 2009.
  4. Identification of genes associated with multiple cancers via integrative analysis. Division of Biostatistics, Washington University at St. Louis. April 3rd, 2009.
  5. Identifying common transcriptional profiles of neoplastic transformation and progression via integrative microarray analysis. Yale Center for Clinical Investigation. Sep. 29th, 2008.
  6. Regularized meta analysis of cancer microarray. NRC. July 30th, 2008.
  7. Data mining in cancer informatics. Renmin University. June 19th, 2008.
  8. Regularized microarray meta-analysis. ICSA. June 7th, 2008.
  9. Meta analysis of cancer microarray studies. Yale Cancer Center. May 14th, 2008.
  
  
• Please email shuangge.ma AT yale.edu for more information.