Welcome to computational biology group @ UNO computer science
Our general research interests fall into many aspects of computational biology and statisitcal genomics/genetics. Our computational biology research interests include: computational and statistical methods for analyzing genome-wide data, biological network inference, comparative genomics and biological sequence analysis. Our statistical genomics/genetics research interest area includes: applied multivariate analysis, machine learning and applications to genomics data mining. 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.
Recently we are in the process of developing
open-source Graphical User Interface (GUI) pattern discovery software
for biomedical research community. The details of the software and web
interface will come soon...