Poster Titles and Abstracts
Huarong Chen, McMaster University
Pattern Recognition and Its Application in Detection of Customers'
In today's competitive market, the company wants to know what
it should do to meet the demands from its customers or to attract
new customers from a certain region, which kind of strategies
should be employed by the company to maintain a profitable market
share. The important information about customers such as the customer
expenditure, the customer family income, and the customers' family
composition will help the company a lot in its decision making.
In the project sponsored by MITACS and Rogers Communication Inc.
we extract the pattern to predict the customers' children information
based on their buying behaviors. Although the model is particularly
focused on customers of video stores in Calgary region, the data
mining methodologies applied in the model can be easily extended
to the similar models for detecting other customer information.
Ahmed Hossain, University of Toronto
Selecting Differentially Expressed Genes in a Multifactorial
Microarrays are part of a new class of biotechnologies which
allow the onitoring of expression levels in cells for thousands
of genes simultaneously. An important and common question in DNA
microarray experiments is the identifcation of differentially
expressed genes, that is, genes whose expression levels are associated
with a response or covariate of interest. The primary goal of
this study lies to identify differentially expressed genes in
a designed microarray experiment Between male and female group.
To fulfil our purpose of this study we characterize the data by
a statistical model that accounts for relevant sources of variation
in the data and then we consider test statistic values of the
model parameters using appropriate contrast. Here in this case
study we includes reading in the data, data display and exploration,
as well as normalization and differential expression analysis.
Keywords: DNA microarrays, Empirical Bayes, linear models, differential
expression, Multiple Testing, False Discovery Rate.
Gatot Ilhamto, University of Guelph
Neural Networks Application in Fertility Prediction
We evaluate the performance of two predictive models for the
waiting times to first birth among Indonesian women, the logistic
regression and the neural networks. There is no significant difference
between the two models, although the neural networks tends to
give lower misclassification error.
Tanguy Pallaver, Laval University
Data classification from improved self organizing map
We present recent results on the unsupervised improved Kohonen
network on the problem of ordering and classifying data structures.
We address the question if the Kohonen network map can reflect
small world architecture inherent in the data set.
Xu Wang, University of Waterloo
A New Mixture Discriminant model for Drug Discovery Data
In drug discovery, statistical models are a powerful tool for
predicting activity of compounds against biological targets. In
this supervised learning problem, descriptors of molecular structure
(e.g. atomic weight, types of bonds, many other exotic characteristics)
are used to predict activity. The features of drug discovery data
include the rareness of active compounds, multiple mechanisms,
and high dimensional descriptor spaces. Conventional mixture discriminant
methods have difficulty finding the best model for the drug discovery
data due to the complication of data sets and the number of parameters.
It is believed that the biological activity of compounds only
depends on several descriptors, so we introduce a new mixture
model, which has fewer parameters, and seeks to predict using
multiple subspaces (ie multiple mechanisms). The EM algorithm
is used to estimate parameter, in conjunction with carefully chosen
initial values and some other tuning parameters.
Rob Warren, University of Waterloo
Dynamic analysis of social networks
A interesting problem in Social Network Analysis (SNA) is their
resilience to interference and how information flows from one
to another. In the past, we have always approached these problems
from a static or 'snapshot' perspective: all available data was
lumped in the same analysis and a conclusion derived.
Our hyphotesis is that since the world is a dynamic system, the
analysis should either be dynamic itself or at a minimum, conclusions
based on static SNA metrics should be revisited.
We test our assumptions on Gnu Privacy Guard key trust databases,
discuss several examples where the static assumption is counter-productive
and suggest possible alternatives.
Li Xu, Montreal Neurological Institute
Improved method for analyzing MR spectroscopy imaging
Multiple sclerosis is a chronic disease of the central nervous
system. Proton magnetic resonance spectroscopy (MRS) can non-invasively
measure the metabolites in human brain and is helpful in research
of progression of MS. We applied multivariate mixed-effect statistical
models for repeated measurement to analyze the MRS data and modeled
the covariance matrix to take into consideration the spatial correlations
within MRS. The method was applied in a series of studies and
demonstrated that the distribution of brain metabolites was different
among MS patients in different disease phases. These studies also
showed the correlations between the brain metabolites and clinical
data such as disease duration and clinical disability.
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