This website is for the research and demonstration projects I have been carried out by bringing about multiple discipline research to address vital real-world problems, particularly those in bioinformatics, medical imaging, computer vision, economics and education.
Please check the list below for some of my research projects and software.
High-throughput microarray technology is an important and revolutionary technique used in genomics and
systems biology to analyze the expression of thousands of genes simultaneously. The popular use of this
technique has resulted in enormous repositories of microarray data, for example, the Gene Expression
Omnibus (GEO), maintained by the National Center for Biotechnology Information (NCBI). However, an
effective approach to optimally exploit these datasets in support of specific biological studies is still
lacking. Specifically, an improved method is required to integrate data from multiple sources and to
select only those datasets that meet an investigator's interest. In addition, to exploit the full power
of microarray data, an effective method is required to determine the relationships among genes in the
selected datasets and to interpret the biological meanings behind these relationships.
To address these requirements, we have developed a machine learning based approach that includes:
- An effective meta-analysis method to integrate microarray data from multiple sources; the method exploits
information regarding the biological context of interest provided by the biologists. - A novel and effective
cluster analysis method to identify hidden patterns in selected data representing relationships between genes
under the biological conditions of interest. - A novel motif finding method that discovers, not only the common
transcription factor binding sites of co-regulated genes, but also the miRNA binding sites associated with the
biological conditions. - A machine learning-based framework for microarray data analysis with a web application to run common analysis tasks on online.
Publication of this research is available online at ACM Digital Library.
to be filled...
Traditional Online Advertising Systems use user's contents to determine appropriate ads to deliver.
In this project, we apply machine learning to look for insights from user's contents, particularly those they make available on social networks.
This project aims that analyzing social data ...