Intelligent Systems and Bioinformatics Laboratory

Research Interests

• Machine learning, neural networks and their applications on bioinformatics problems.

• Data mining in bioinformatics databases.

• Normalization and pre-processing methods for microarray data.

• Novel methods of selection of differentially regulated genes.

• Extracting functional information from gene expression data using machine learning and neural network techniques.

• Methods for the assessment of cluster confidence of microarray data.

• Methods for the assessment of statistical significance of differences between sets of microarray results (e.g. cancer vs. healthy patients)
  
  

Current Research

Bioinformatics:

Functional analysis of high throughput gene expression experiments:

• Onto-Express collaboration with Stephen Krawetz (Version 1 paper)
  (Version 2 paper)

• Onto-Compare (paper)

• Onto-Design (paper)

• Onto-Translate (paper)

• Onto-Miner

• TAQ (paper)

Data Analysis Techniques for DNA and protein microarrays:

• Methods for the selection of differentially regulated genes  (paper)

• Protein chips for ovarian cancer screening and diagnosis with Michael Tainsky

• Gene expression changes in cell immortalization with Michael Tainsky (paper)

• Gene expression changes in epilepsy with Jeff Loeb

• Gene expression changes significance analysis with BioDiscovery (paper)

Image processing:

• Improved segmentation for real time fMRI with Stefan Posse

• Segmentation and visualization methods for PET images with Otto Muzik, PET Center 

Neural networks:

• Applications of Kolmogorov's superposition theorem in neural networks with David Sprecher (paper)