News
Jan. 25, 2012
Our gene sets based network inference papers published in Bioinformatics and TCBB.

Dec. 22, 2011

Dr. Lipi Acharya graduated with a PhD and landed her first job at Dow AgroSciences.

Sept. 16, 2011
Computer lab is being set up on the thrid floor of Maccabees Building.

Aug. 18, 2011
We have moved to Wayne Syate University Computer Science Department.

Links

Welcome to CBDM @ Wayne

Our general research interest falls into methodological and algorithmic aspects of computational biology and data mining. For a brief description about our research, please read along. For more details, please read our research page. In addition, we actively collaborate with biomedical researchers to develop and apply tailor-made computational and statistical approaches to solve real-world biomedical problems. Our current research thrusts are as follows:

One: Biological network inferences using high throughput molecular profiling data. Biological networks are the primary mean to regulate cell growth, differentiation and apoptosis. Unfortunately, it is nearly impossible to observe network structures from experiments. Therefore, reverse engineering is a viable approach to uncover the underlying bio-complexity. We develop and implement innovative models, algorithms and software for inferrring biological network structures. We also develop novel predictive models as well as signal transduction events to predict phenotypes from network structures.     

Two: Data mining and pattern recognition models, algorithms and software for high throughput molecular profiling data. More specifically, we attempt to tackle some statistical/mathematical/computational issues arising from large p, small n paradigm using dimension reduction approaches. We also develop feature extraction and discriminative analysis approaches to anallyze next-generation sequencing data.

Three: Open-source Graphical User Interface (GUI) bioinformatics software tools development. Our current efforts concerns the transacriptome de novo assembly and quantification using RNA-seq. To this end, we have developed a GUI pipeline implmeting novel in-house develped algorithms for mRNA transcript identification and quantification. For more information on the pipeline, please refer to the following sourcefoge site: http://asammate.sourceforge.net.