Brief Description of projects
 |
Feature selection in micro array gene expression data Our aim is to develop filter-based feature selection techniques suitable for datasets with high dimensionality and large number of classes or more specifically, multiclass gene expression datasets such as the Global Cancer Map (GCM) and the NCI60 datasets. The techniques should be able to produce feature subsets or predictor sets capable of giving suitably high classification rates, while using a minimal number of features. |
Team Members: Chia Huey Ooi (Doctoral student -Completed), Dr.Madhu Chetty (Main Supervisor), Dr. Shyh Wei Teng (Associate Supervisor)
|
Hybrid computational models for protein sequence analysis and secondary structure prediction |
The project aims to develop novel hybrid computational models for protein sequence analysis and secondary structure prediction. These models will be used to explore profiles for homologous proteins, structure prediction, missing data estimation and inferring of phylogenetic trees.
Team Members: Niranjan Bidargaddi (Doctoral student- Completed), Dr. Madhu Chetty (Main Supervisor), Dr. J. Kamruzzaman (Associate Supervisor)
|
New Strategies for Ab Initio Protein folding prediction |
The objectives are to develop theoretical underpinnings and a systematic methodology for 2D HP Model for improving the accuracy including fitness evolution and computational throughput in Protein Folding Prediction (PFP), extend the novel strategies developed earlier for 2D HP model into 3D model to predict the ab initio three dimensional structure of a protein from its primary amino acid sequence. Further, we also aim to develop strategies for handling large amino acid sequences.
Team Members: Tamjidul Hoque (Doctoral student- Completed), Dr. Madhu Chetty (Main Supervisor), Professor L. S. Dooley (Associate Supervisor)
|
Modeling and Inferencing of Gene regulatory network |
The aim of the project are to primarily look into the modeling and inferencing of GRNs based on Causal models and Genetic Algorithm, and their subsequent implementation and testing using real temporal mRNA data sets (such as yeast), available in public domain. Understanding of GRNs is still at a relatively embryonic stage and is the next big break for computational biology. It is very difficult to understand biological systems because of their inherent complexities. The main significance of this project will be the development of novel algorithms and techniques for modeling that will help in successful deduction of the said functions for each gene ‘node’, which in turn will lead to more accurate modeling behaviour of a cell.
Team Members: Ramesh Ram (Doctoral student-Ongoing), Dr. Madhu Chetty (main Supervisor), Associate Professor Trevor Dix (Associate Supervisor)