Bioinformatics Research Group at BI Lab

The Bioinformatics Research Group at the Biointelligence Lab is interested in developing and applying machine learning algorithms for the analysis of genomic and proteomic data with a specal emphasis on DNA chip data mining. Current research focuses on probabilistic graphical models, including hidden Markov models (HMMs), Bayesian networks, Helmholtz machines, latent variable models, and generative topographic mapping.

Upcoming Conferences on Bioinformatics 




Invited Talks

Research Projects

  • ProMiR: In silico Probabilistic Prediction of microRNA
  • AptaCDSS II: In silico A Clinical Decision Assistant System with Knowledge Discovry - A Post AptaCDSS System
  • DNAChipBench (NRL Project): In silico Intelligent Design and Analysis Technology for DNA Chips
  • AptaCDSS: In silico A diagnosis support system for cardiovascular disease using aptamer chip
  • SysBio: In silico modeling and network construction of chromosomal replication and segregation
  • BrainGene : DNA data mining for the analysis of expression patterns of vertebrate brain development-specific genes (rat)
  • AngioProt : Development of prediction algorithm for angiogenesis-related molecules
  • DMDM: DNA microarray data mining using unsupervised learning techniques
  • ProClass: Protein classification based on molecular sequence and text information
  • BioText: Information extraction from biological texts based on Markov models
  • MicroGene: Microbial gene identification using probabilistic graphical models

Useful Links


This page is maintained by Je-Keun Rhee (jkrhee |at|
Last update: August. 6, 2009.