RESEARCH PROJECTS:

 

·         Develop ‘WordSeeker’ platform for DNA motif finding. This is a software suite which implements many statistically methods for pattern recognitions and DNA signatures findings. Lonnie Welch, School of EECS, Ohio University. Stephen Lee, Dept of Statistics, University of Idaho.

·         Genomic Properties of the Bidirectional Promoters and the Unidirectional Promoters of the Human DNA-repair Pathway.

o   Biological Background: bidirectional promoters are DNA repair pathways which are linked with certain types of cancer.

o   Hypothesis: the promoters of genes involved in DNA repair pathways have unique properties (signatures), and there are genomic differences between bidirectional promoters and unidirectional promoters.

o   Goals: characterize the promoter regions of genes involved in DNA repair pathways; identify specific genomic differences between bidirectional promoters and unidirectional promoters; identify genomic signatures for each class of promoter (bidirectional and unidirectional).

o   Collaborators: Laura Elnitski, Genomic Functional Analysis Section, National Human Genome Research Institute, NIH. Lonnie Welch, School of EECS, Ohio University. Stephen Lee, Dept of Statistics, University of Idaho.

·         Genomic Properties of the Intergenic Regions of Arabidopsis Thaliana.

o   Biological Background and Hypothesis: there are many undiscovered functional elements and patterns in the intergenic regions of Arabidopsis.

o   Goals: identify statistically overrepresented and underrepresented elements and patterns in the entire intergenic region of Arabidopsis

o   Collaborators: Erich Grotewold, Dept of Plant Cellular and Molecular Biology, Plant Biotechnology Center, OSU. Lonnie Welch, School of EECS, Ohio University. Stephen Lee, Dept of Statistics, University of Idaho.

 

 

REFEREED PUBLICATIONS:

 

(2009)

1.      Frank Drews, Klaus Ecker, Stephen Lee, Laura Elnitski, Lonnie R Welch:Word-based Characterization of Promoters Involved in Human DNA Repair Pathways”. Journal of BMC Genomics. 2009, 10

2.      Jens Lichtenberg, Alper Yilmaz, Joshua D Welch, Kyle Kurz, Xiaoyu Liang, Frank Drews, Klaus Ecker, Stephen Lee, Erich Grotewold, Lonnie R Welch:  The Word Landscape of the non-coding segments of the Arabidopsis thaliana Genome.  Journal of BMC Genomics. 2009, 10

3.      James R. Conrad, Jim Alves-Foss, Stephen Lee : Analyzing Uncertainty in Take-Grant Protection Graphs with TG/MC. Accepted for publication in the  Journal of Computer Security.

(2008)

1.      Stephen Lee: Seeking Significant Oligomers via Set Partitions Expected Count. International Journal of Computational Science, 2008, Vol. 2, No. 5, 584-598.

2.      Christina L. Airhart, Harold N. Rohde, Gregory A. Bohach, Carolyn J. Hovde, Claudia F. Deobald, Stephen Lee, Scott A. Minnich:  Induction of Innate Immunity by Lipid A Mimetics Increases Survival from Pneumonic Plague” Journal of Microbiology 2008, 154, 2131–2138.

3.      Christina L. Airhart, Harold N. Rohde, Carolyn J. Hovde, Gregory A. Bohach, Claudia F. Deobald, Stephen Lee, Scott A. Minnich: “Lipid A Mimetics are Potent Adjuvants for an Intranasal Pneumonic Plague Vaccine” Journal of Vaccine. 2008, Oct 16; 26(44):5554-61.

(2006)

1.      Shanyu Zheng, Jim Alves-Foss, Stephen Lee : The Effect of Rebalancing on the Performance of a Group Key Agreement Protocol,  The 2nd IEEE LCN Workshop on Network Security.

(2005)

1.      Shanyu Zheng, Jim Alves-Foss, Stephen Lee : Exploring Average Performance of Group Key Management Algorithms Over Multiple Operations. International Conference on Communications, Internet and Information Technology.

2.      Shanyu Zheng, Jim Alves-Foss, Stephen Lee: Performance of Group Key Agreement Protocols Over Multiple Operations. International Conference on Parallel and Distributed Computing and Systems.

(2004)

1.      Zhaofei Fan, Stephen Lee, Stephen R. Shifley, Frank R Thompson III, and David R. Larsen. “Simulating the effect of landscape size and age structure on cavity tree density using a resampling technique.” Forest Science, Number 5, October 2004 , pp. 603-609(7).

(2003) and prior

1.      Lee, S. (2001) ``On improving the binary classification accuracy of quadratic discriminant.'' Journal of Statistical Computation and Simulation, 2001.

2.      Lee, S. (2000) ``Noisy replication in skewed binary classification.'' Computational Statistics & Data Analysis, August, 2000.

3.      Lee, S. (1999) ``Regularization in skewed binary classification.'' Computational Statistics, 1999.

4.      Williams, C., Lee, S., Fisher, R., and Dickerman, L. (1999) `` A comparison of statistical methods for prenatal screening for Down syndrome.'' Applied Stochastic Models in Business and Industry, 1999.

5.      Lee, S. (1998) ``Regularized tree methods for skewed binary classification.'' Journal of Statistical Computation and Simulation, 1998.

6.      Lee, S. (1997) ``Regularized quadratic discriminant methods for skewed binary classification.'' Journal of Statistical Computation and Simulation, 1997.

7.      Lee, S. (1996) ``Combining models to improve classification rates.'' Journal of Statistical Computation and Simulation, 1996.

8.      Lee, S. (1996) ``On a class of nonlinear time series for biological population abundance data.'' Applied Stochastic Models and Data Analysis, Vol 12, 193-207, 1996.

9.      Chenoweth, T., Obradovic, Z. and Lee, S. (1996) ``Embedding Technical Analysis into Neural Network Based Trading Systems.'' Applied Artificial Intelligence, vol 10, no. 3., 1996.

 

 

PROCEEDINGS/TECHINCAL REPORTS:

 

1.      Lee, S. (2001) “Predicting the Growth Origin of Potatoes” Statistical Consulting Center Technical Report, July, 2001.

2.      Lee, S. and Elder, J. (1997) “Bundling Heterogeneous Classifiers using Advisor Perceptrons.”  Technical Report #97-01, ELDER RESEARCH, Charlottesville, Virginia, 1997.

3.      Lee, S. (1997) “Regularization in skewed binary classification.”  Proceedings of the American Statistical Association joint statistical meetings, 1997.

4.      Lee, S. (1996) “Combining neural and statistical classifiers via perceptron.”  Proceedings of the American Association for Artificial Intelligence-96, Portland, OR.

5.      Lee, S. (1995) “Predicting atmospheric ozone using neural networks as compared to some statistical methods.”  Proceedings of the IEEE Technical Applications Conference, Nortcon, Portland, OR.

6.      Chenoweth, T., Obradovic, Z. and Lee, S. (1995) “Technical trading rules as a prior knowledge to a neural networks prediction system for the S&P 500 index.”  Proceedings of the IEEE Technical Applications Conference, Nortcon, Portland, OR.

7.      Lee, S. (1992) “A nonlinear auto-regressive time series model.”  Proceedings of the American Statistical Association joint statistical meetings, Business and Economics Section, pp. 327-330, 1992.

 

 

SEMINAR/PAPERS/POSTERS PRESENTED AT NATIONAL SCHOLARLY MEETINGS:

 

1.      Invited Seminar, Workshop on Special Topics in Bioinformatics.  Toronto, Canada,  July 19-22, 2008. The 16-th Annual International Conference on Intelligent Systems for Molecular Biology.

2.      Poster Presentation.  Frank Drews, Klaus Ecker, Kyle Kurz, Stephen Sauchi Lee, Xiaoyu Liang, Jens Lichtenberg, Joshua D. Welch, and Lonnie R. Welch : “WordSeeker: A High Performance Bioinformatics Pipeline for Computational Regulatory Genomics”, 2008

3.      Invited Seminar, Workshop on Special Topics in Bioinformatics.  Salt Fork, Ohio,  Sep 20/21, 2007:  Stephen Lee:  “Computing Oligomer Expected Count through Set Partitions”

4.      Modeling and predicting cavity tree density. Presented at 2005 American Statistical Association joint statistical meetings.

5.      Regularization in skewed binary classification.  Presented at 1997 American Statistical Association joint statistical meetings.

6.      Capabilities and limitations of artificial neural networks. Presented at the University of Idaho, 1997.

7.      Combining neural and statistical classifiers via preceptron.  Presented at 1996 American Association for AI-1996 workshop.

8.      Regularization in skewed binary classification. Presented at the joint applied statistics seminar of the University of Idaho and Washington State University, 1996.

9.      Predicting atmospheric ozone using neural networks as compared to some statistical methods.  Presented at 1995 IEEE Technical Application Conference.

10.  Evaluating the predictive performance of artificial neural networks and statistical models. Presented at the joint applied statistics seminar of the University of Idaho and Washington State University, 1995.

11.  On Box and Jenkins time series models. Presented at Washington State University, 1994.

12.  A nonlinear auto-regressive time series model.  Presented at American Statistical Association 1992 Joint meetings.

13.  Estimation and testing of some nonlinear time series models.  Presented at the Biometrics Society Spring meeting, March 1991.

14.  Stochastic models obtained from some difference equations.  Presented at the American Statistical Florida Chapter Meeting, January 1991.

 

 

 


Two students' drawing of me:

 

 

A Sleepless Night, 1986


 


 

 

 

 

 

A Friend of Mathematics, 1986

 

 



 


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