Welcome to computational biology group @ UNO computer science

Our general research interests fall into many aspects of bioinformatics and biostatistics.  Our bioinformatics research interests include: computational and statistical methods for analyzing genome-wide data, biological network inference, comparative genomics and biological sequence analysis. Our applied statistics research interest area includes: applied multivariate analysis, statistical inference and computing, machine learning. In addition, we actively collaborate with biomedical researchers to apply appropriate computational and statistical techniques to solve real-world biomedical problems.

One of our current research concerns reverse engineering methodology to infer biological pathways and networks from high throughput data. Biological pathways/networks serve as a primary means to regulate cell growth, differentiation and apoptosis. Unfortunately, it is difficult to obtain data that directly reveal network topology and so reverse engineering is a viable method to uncover the underlying bio-complexity. Another research interest concerns developing and tayloring data mining and pattern recognition methods to analysize genome-wide data. More specifically, We try to address some statistical/mathematical issues arose from large p, small n paradigm. For example, using constrained learning and/or shrinkage method, where constrained is inspired from real-world biology prior knowledge or network topology.