Affiliation: Duke University Medical Center
- The construction and assessment of a statistical model for the prediction of protein assay dataJ Pittman
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708, USA
J Chem Inf Comput Sci 42:729-41. 2002..In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented...
- Bayesian analysis of binary prediction tree models for retrospectively sampled outcomesJennifer Pittman
Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708 0251, USA
Biostatistics 5:587-601. 2004..Shortcomings of the approach and comparison with alternative tree modelling algorithms are also discussed, as are issues of modelling and computational extensions...
- Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomesJennifer Pittman
Institute of Statistics and Decision Sciences, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA
Proc Natl Acad Sci U S A 101:8431-6. 2004..This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis...
- Gene expression phenotypes of atherosclerosisDavid Seo
Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA
Arterioscler Thromb Vasc Biol 24:1922-7. 2004..To that end, our group has developed a nonbiased approach congruent with the multigenic concept of complex diseases by identifying gene expression patterns highly associated with disease states in human target tissues...
- Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes predictionJoseph R Nevins
Department of Molecular Genetics and Microbiology, Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC 27710, USA
Hum Mol Genet 12:R153-7. 2003..Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes...
- Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancersAndrew Berchuck
Department of Obstetrics and Gynecology Division of Gynecologic Oncology, Institute of Statistics and Decision Sciences, Center for Applied Genomics and Technology, Duke University Medical Center, Durham, North Carolina, USA
Clin Cancer Res 11:3686-96. 2005..The objective of this study was to define gene expression patterns associated with favorable survival...
- Gene expression phenotypic models that predict the activity of oncogenic pathwaysErich Huang
Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina 27710, USA
Nat Genet 34:226-30. 2003....
- Gene expression predictors of breast cancer outcomesErich Huang
Koo Foundation Sun Yat Sen Cancer Centre, Taipei, Taiwan
Lancet 361:1590-6. 2003..We aimed to predict nodal metastatic states and relapse for breast cancer patients...
- High insulin-like growth factor-2 (IGF-2) gene expression is an independent predictor of poor survival for patients with advanced stage serous epithelial ovarian cancerRobyn A Sayer
H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MCC GYNPROG, Tampa, FL 33612 9497, USA
Gynecol Oncol 96:355-61. 2005..The purpose of the current study is to further elucidate the role of the IGF-2 gene in ovarian cancer development and progression...
- Genomic prediction of locoregional recurrence after mastectomy in breast cancerSkye H Cheng
Department of Radiation Oncology, Koo Foundation Sun Yat Sen Cancer Center, Pei tou District, Taipei, Taiwan
J Clin Oncol 24:4594-602. 2006..This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy...