| Kernelized partial least squares for feature reduction and classification of gene microarray data WH Land, X Qiao, DE Margolis, WS Ford, CT Paquette, JF Perez-Rogers, ... BMC systems biology 5, 1-18, 2011 | 15 | 2011 |
| Systems and methods for tissue classification using attributes of a biomarker enhanced tissue network (BETN) A Santamaria-Pang, DE Margolis US Patent 8,831,327, 2014 | 14 | 2014 |
| Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory WH Land, D Margolis, R Gottlieb, EA Krupinski, JY Yang BMC genomics 11, 1-11, 2010 | 11 | 2010 |
| A kernel approach for ensemble decision combinations with two-view mammography applications WH Land Jr, D Margolis, M Kallergi, JJ Heine International Journal of Functional Informatics and Personalised Medicine 3 …, 2010 | 11 | 2010 |
| Investigating the GRNN Oracle as a method for combining multiple predictive models of colon cancer recurrence from gene microarrays AS Campbell, WH Land, D Margolis, R Mathur, D Schaffer Procedia Computer Science 20, 374-378, 2013 | 8 | 2013 |
| A complex adaptive system using statistical learning theory as an inline preprocess for clinical survival analysis D Margolis, WH Land Jr, R Gottlieb, X Qiao Procedia Computer Science 6, 279-284, 2011 | 7 | 2011 |
| Tissue segmentation and classification using graph-based unsupervised clustering D Margolis, A Santamaria-Pang, J Rittscher 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 162-165, 2012 | 5 | 2012 |
| A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored/non-censored (event) data WH Land Jr, X Qiao, D Margolis, R Gottlieb Procedia Computer Science 6, 267-272, 2011 | 5 | 2011 |
| Crowdsourcing with complex workers: Utilizing prior knowledge of worker experience and active learning DE Margolis State University of New York at Binghamton, 2013 | | 2013 |
| Using Statistical Learning Theory to Improve Treatment Response for Metastatic Colorectal Carcinoma WH Land, D Margolis, R Gottlieb | | 2010 |
| Evaluating response to treatment and predicting outcome in patients with metastatic colorectal carcinoma using statistical learning theory DE Margolis State University of New York at Binghamton, 2010 | | 2010 |
