| 09/04/02 | Optimally weighted statistic for combining multiple genomic studies. | Department of Statistics, University of Pittsburgh |
| 08/07/24 | Issues in Combining Multiple Genomic Studies. | Institute of Statistical Science, Academia Sinica, Taiwan |
| 08/07/14-19 | Nonparametric meta-analysis for identifying signature genes in the integration of multiple genomic studies | 7th World Congress in Probability and Statistics, Singapore |
| 08/06/4-7 | An optimal-weight statistic for meta-analysis of multiple genomic studies | 17th ICSA Applied Statistics Symposium |
| 08/03/16-19 | Inferring the true correlation in cross-species microarray data. | ENAR 2008, Arlington, Virginia |
| 08/02/13 | Meta-analysis for cross-prediction and DE gene detection in multiple genomic data sets. | Simmons Center for Interstitial Lung Disease, UPMC |
| 07/07/29 | Statistical framework for integrative analysis of multiple gene expression. | JSM 2007, Salt Lake City, Utah |
| 07/06/27 | Statistical Framework for Integrative Analysis of Multiple Gene Expression Profiles. | 2007 Taipei International Statistical Symposium, Academia Sinica, Taiwan. |
07/06/22
07/06/21 | One-day miniworkshop: Microarray Data Analysis (link)
Meta-analysis for multiple microarray data sets. | Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. |
| 06/11/28 | Statistical integrative analysis of multiple expression profile and biological data: two examples in cancer and aging. | Dept. of Computational Biology, University of Pittsburgh. |
| 06/09/07 | Which Missing Value Imputation Method to Use in Expression Profiles: a Comparative Study and Two Selection Schemes. | Dept. of Biostatistics, University of Pittsburgh. |
| 06/06/15 | Comparative study of gene clustering in microarray and penalized and weighted K-means. | ICSA 2006 Applied Statistics Symposium. |
| 06/03/01 | Evaluation and comparison of gene clustering methods in microarray analysis.(slides) | Dept of Statistics, Texas A&M. |
| 05/12/23 | Integrated Clustering and Classification Analysis for Learning Inducing Structural Motifs contributing to MS/MS Fragmentation Patterns. | Dept of Statistics, National Chiao Tung University. |
| 05/12/21 | Opportunities and challenges in Biostatistics and Bioinformatics for Math major students. (slides) | Dept of Mathematics, National Taiwan University. |
05/12/20
05/12/21 | Penalized and Weighted K-means for Clustering with Noises and Prior Information Incorporation | Institute of Statistical Science, Academia Sinica.
Dept of Mathematics, National Taiwan University. |
| 05/11/02 | A generalized form of K-means. (slides) | Neyman Seminar, Dept of Statistics, UC Berkeley. |
| 05/08/08 | Penalized and weighted K-means. | JSM 2005 |
| 04/12/15 | Tutorial: Statistical analysis and software for Affymetrix GeneChip arrays and some recent advances. (slides) (R code)
Tutorial: Classification and clustering problems in microarray analysis and some recent advances. (slides) | 2004 Taipei Symposium on Statistical Genomics (Academia Sinica, Taiwan) |
| 04/12/14 | A data mining scheme for identifying peptide structural motifs behind different MS/MS fragmentation intensity. | National Health Research Institutes, Taiwan |
| 04/12/07 | A comparative review of gene clustering in expression profile. (slides) | ICARCV 2004 at Kunming |
| 04/08/10 | Tight Clustering and Penalized Weighted K-means applied in genomic research. | Laboratory of DNA Information Analysis, University of Tokyo |
| 04/06/02 | Tight Clustering: a method for extracting stable and tight patterns in expression profiles. | IPAM Functional Genomics 2004 Reunion Conference (UCLA) |
| 04/05/10 | Tight Clustering: a method for extracting stable and tight patterns in expression profiles.(slides) | International Conference on Analysis of Genomic Data (Harvard University) |
| 03/12/15~20 | Tight Clustering: a method for extracting stable and tight patterns in expression profiles. | National Taiwan University
Academia Sinica
National Chiao Tung University |
| 03/08/03 | A method for tight clustering: with application to microarray. | Joint Statistical Meetings 2003 |
| 01/06~02/04 | Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. (slides) | UCLA, Brighan Women Hospital, Harvard University, MIT |
