Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences
Limin Li (firstname.lastname@example.org)
Motoki Shiga (email@example.com)
Wai-Ki Ching (firstname.lastname@example.org)
Hiroshi Mamitsuka (email@example.com)
 Advanced Modeling and Applied Computing Laboratory, Department of
Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong
 Bioinformatics Center, Institute for Chemical Research, Kyoto
University, Gokasho, Uji 611-0011, Japan
Annotating genes is a fundamental issue in the post-genomic era.
A typical procedure for this issue is first clustering genes by their
features and then assigning functions of unknown genes by using known
genes in the same cluster.
A lot of genomic information are available for this issue, but two major
types of data which can be measured for any gene are microarray
expressions and sequences, both of which however have their own flaws.
Thus a natural and promising approach for gene annotation is to
integrate these two data sources, especially in terms of their costs to
be optimized in clustering.
We develop an efficient gene annotation method with three steps
containing spectral clustering over the integrated cost, based
on the idea of network modularity.
We rigorously examined the performance of our proposed method from three
All experimental results indicate the performance advantage of our
method over possible clustering/classification-based approaches of gene
function annotation, using expressions and/or sequences.
Japanese Society for Bioinformatics