Oct 28, 2008
2:30pm - 3:30pm

Goodwin 524

Ranking single nucleotide polymorphisms by potential deleterious effects

Phil Lee

Identifying single nucleotide polymorphisms (SNPs) that are responsible for common and complex diseases, such as cancer, is of major interest in current molecular epidemiology. However, due to the tremendous number of SNPs on the human genome, to expedite genotyping and analysis, there is a clear need to prioritize SNPs according to their potentially deleterious effects to human health. As of yet, there have been few efforts to quantitatively assess the possible deleterious effects of SNPs for effective association studies. Here we propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects in a probabilistic framework. We also provide the evaluation result of our system on the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely used databases of human genes and genetic disorders.

Nov 11, 2008
2:30pm - 3:30pm

Goodwin 524

Acoustic Emissions of Handwriting

Andrew Seniuk

Handwriting and speech recognition are problems with a long history. However, no studies have considered the sounds produced by handwriting, an information source which has connections to both of the aforementioned. This presentation will summarise my work in pen acoustic emissions, including a few demonstrations, early results on recognition of cursive handwritten characters, other possible applications, and some hypotheses for discussion.

Jan 13, 2009
2:30pm - 3:30pm

Goodwin 524

Selecting single nucleotide polymorphisms for effective genetic association study

Phil Lee

Genetic variation analysis holds much promise as a basis for understanding disease-gene association. In particular, single nucleotide polymorphisms (SNPs) are at the forefront of such studies, as they are the most common form of DNA variation on the genome. However, due to the tremendous number of candidate SNPs, there is a clear need to expedite genotyping and analysis by selecting and considering only a subset of all SNPs. In this talk, I will present several successful applications of machine learning to address the problem of SNP selection and to improve current state-of-the-art SNP selection methods. Our first method is based on the tag SNP selection approach, which aims to select a subset of SNPs whose allele information can best represent the allele information of unselected SNPs. Using the formalism of Bayesian networks, the proposed method is able to select a subset of independent and highly predictive SNPs, without limiting the number or the location of predictive tag SNPs. Our second method is based on the functional SNP selection approach, which aims to directly select a subset of SNPs that are likely to be disease-causing. In the probabilistic framework, our integrative scoring system combines the functional assessments from a variety of bioinformatics tools, and prioritizes SNPs according to their potential deleterious effects to human health. Last, I describe our new multi-objective optimization framework for identifying SNPs that are both informative tagging and have functional significance.

Mar 17, 2009
2:30pm - 3:30pm

Goodwin 524

Identifying Common Substructural Patterns of Protein Contact Maps

Hazem Ahmed

1D protein sequences, 2D contact maps and 3D structures are three different representational levels of detail for proteins. Predicting protein 3D structures from their 1D sequences remains one of the complex challenges of bioinformatics. The “Divide and Conquer” principle is applied in our research to handle this challenge, by dividing it into two separate yet dependent subproblems, using a Case-Based Reasoning (CBR) approach. Firstly, 2D contact maps are predicted from their 1D protein sequences; secondly, 3D protein structures are then predicted from their predicted 2D contact maps. We focus on the problem of identifying common substructural patterns of protein contact maps, which could potentially be used as building blocks for a bottom-up approach for protein structure prediction. We further demonstrate how to improve identifying these patterns by combining both protein sequence and structural information. We assess the consistency and the efficiency of identifying common substructural patterns by conducting statistical analyses on several subsets of the experimental results with different sequence and structural information.