Muhammad Shoaib B. Sehgal

Research

Research Statement:

Machine Learning and Mathematical/Statistical modelling methods are widely used in various biological applications, ranging from diagnosis to drug discovery. Knowledge discovery from high throughput biological data using these methods however, is a challenging task due to noise, high dimensionality and significant number of missing values in the data. My research aspires to develop post genomic knowledge inference methods for tailored drug target identification which can be used by pharmaceutical industry for tailored drug discovery in order to cure diseases which are not cure able by traditional drugs such as, cancer.

My research objectives include:

1- Drug Target Identification techniques using Gene Regulatory Network Modelling

2- Differentially Expressed Gene Selection Methods using stochastic techniques

3- Diagnosis using Class Prediction Methods

4- Missing Value Estimation Methods for Gene Expression Data

To achieve above mentioned research objectives, I have used and developed various Statistical/Mathematics and Computational Intelligence methods to model Gene Regulatory Networks, Cross Platform Data Fusion, Clustering, Class Prediction, Gene Selection and Missing Value Imputation techniques for high throughput genetic expression data.

Research Interests

Bioinformatics
Functional Genomics
Gene Networks
Neural Networks
Support Vector Machines
Decision Based Fusion
Pervasive Computing
Ambient Intelligence
Speech and Signal Processing