Abstracts Keynote & Plenary

natural structure is coded in its amino acid
sequence. The way this sequence folds in the 3D space are very important, and can be used for
determining protein function. Protein structures are stored in the Protein Data Bank (PDB). With the
rapid growth of technology, the number of determined protein structures increases every day, so,
retrieving structurally similar proteins using current algorithms takes too long (hours or even days). So,
improving the efficiency of protein structure retrieval and classification methods becomes an important
research issue in bioinformatics community.
In this paper, a novel protein classifier is presented.
Our classifier uses the information about the
conformation of protein structures in 3D space. Namely, the voxel based protein descriptor is used for
representing protein structures. The 3D Discrete Fourier Transform is applied to protein tertiary
structures in order to produce geometry based descriptors. Additionally, some properties of the primary
and secondary structure of the protein are considered, thus forming better integrated descriptor.
Part of the SCOP 1.73 database was used for evaluation of our classifier. The results show that
our
approach achieves more than 78.83% classification accuracy and that it is much faster than other
similar algorithms with comparable accuracy. We provide some experimental results.
Keywords: PDB, SCOP, protein classification, Support Vector Machine (SVM).
OR-021
An Association
Test for Multiple Traits Based on the Generalized Kendall’s Tau
Xueqin Wang
Department of Medical
Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University,
Guangzhou 510080, China
In many genetics studies, especially
in the investigation of mental illness and behavioral disorders, it is
common for researchers to collect multiple phenotypes to characterize the complex disease of interest.
It may be advantageous to analyze those phenotypic measurements simultaneously if they share a
similar genetic mechanism. In this study, we present a nonparametric approach to studying multiple
traits together rather than examining each trait separately. Through simulation we compared the
nominal type I error and power of our proposed test to an existing test, i.e., a generalized family-based
association test. The empirical results suggest that our proposed approach is superior to the existing test
in the analysis of ordinal traits. The advantage is demonstrated on a data set concerning alcohol
dependence. In this application, the use of our methods enhanced the signal of the association test.
OR-022
A Series of Studies for Systems Biology of Complex Diseases
Shaoqi Rao,
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University,
Guangzhou 510080, China
The central theme of biomedical
domians is how to use modern concepts and methods in systems
biology, and high- throughput omics technologies to unravel the underlying genetic mechnisms for
various complex diseases influential on human health, to discover the key genes or interactions, and
finally to develop new methods and technologies for improved medical diagnosis and treatment. In last
five years, the speaker and his associated bioinformatics team have been actively involved in the
frontiers in cardiovascular genetics, statistical genomics and statistics, bioinformatics and systems
biology. This report briefly describes the recent studies undertaken by this team, separated into: (1)
applied