Online Resources for Zhenyu's Research

In recent years, DNA microarray technology has proven to be a very powerful tool for simultaneously monitoring the expression of several thousands of genes. Analysing the large amount of gene expression data from microarray chips can play a very important role in biology medicine, especially in cancer diagnosis. It also offers an opportunity and a challenge for current machine learning research. Clustering analysis and some other statistical methods have been established as primary tools for the analysis of microarray data. Some other supervised learning approaches, such as Support Vector Machines (SVM), Multi-Layer Perceptions (MLP) and Decision Trees (DT) are also widely used in practice. In this study, we attempt to use a different tool, Neuro-Fuzzy models (NF). Our current experimental results show that our NF and NFE models can be used as efficient computational tools for microarray data analysis. In addition, compared to some current most widely used approaches, NF/NFE models not only give a good classification result, but their behavior can also be explained and interpreted in human understandable terms, which can provide the researchers with a better understanding of the data.

My research covers a number of areas, including:

• Neuro-Fuzzy Systems
• Microarray Analysis
• Ensemble Learning
• Evolutionary Algorithms

Some other resources for above areas:

• Data Sets
• Software
• Conferences and Journals
• People

Last update: 26 March 2006