Special Sessions

Call for Special Sessions

The OSB 2008 Program Committee is inviting proposals for special sessions to be held during the conference (http://www.aporc.org/OSB/2008/), taking place on November 1-4, 2008, in Lijiang, China. Proposals for special sessions should be submitted in ELECTRONIC FORMAT to:

Ling-Yun Wu, Ph.D.
Associate Professor, Academy of Mathematics and Systems Science
Chinese Academy of Sciences
No. 55, Zhongguancun East Road, Haidian, Beijing 100080, China
Tel: +86-10-62616659
Email: lywu@amt.ac.cn

The special session organizer(s) contact information should also be included. Each special session organizer will be session chair for their own special sessions at the conference accordingly.

Papers in special sessions should be submitted electronically via Online Submission System. All submitted papers will be refereed by experts in the respective fields according to the criteria of originality, significance, quality, and clarity. The special session will be officially accepted by the conference on the condition that there are at least 4 papers accepted by peer-reviewers. So the organizers for the prospective special sessions are welcome to invite more authors to submit their works to the conference.

Special session proposal deadline is: May 31, 2008

Special Sessions

The approved special sessions are listed as follows:

1. Symbolic Computation in Systems Biology

Chair: Katsuhisa Horimoto
Computational Biology Research Center
National Institute of Advanced Industrial Science and Technology, Japan
Email: k.horimoto@aist.go.jp

Recently, symbolic computation has been intensively utilized to address the biological issues, and this new field emerges, named "Algebraic Biology". Indeed, the algebraic biology covers various issues in a wide range of biological fields from molecular biology to population biology by various techniques of symbolic computation. This session will focus on the application of symbolic computation to the issues in systems biology, especially the analyses of system structure and behavior by symbolic computation.

2. Dimension Reduction in Bioinformatics

Chair: Guo-zheng Li, Ph.D.
Associate Professor, School of Computer Engineering & Science
Shanghai University, 149, Yanchang Road, Shanghai, 200072, China
Tel: +86-21-5633-5263
Fax: +86-21-5633-3061
E-mail: gzli@shu.edu.cn
Homepage: http://www.cs.shu.edu.cn/gzli

Dimension reduction are special optimization techniques, which will help to remove noisy features and reduce the dimensionality of biological data sets, they have been studied widely especially in micro-array analysis because the high dimensionality of micro-array data sets hurts generalization ability of machine learning methods. Research in mathematics, statistics, computer science, engineering and bioinformatics confront similar issues in feature selection and extraction, and we see a pressing need for and benefits in the interdisciplinary exchange and discussion of ideas. We anticipate that our collaborations will shed light on research directions and provide the stimulus for creative breakthroughs.

This special session will bring together researchers from different disciplines and encourage collaborative research in feature selection and extraction. Dimensionality reduction including feature selection and extraction is an essential step in successful bioinformatics applications; it has practical significance in many areas such as bioinformatics, statistics, pattern recognition, machine learning, and data mining. The objectives of feature selection and extraction include: building simpler and more comprehensible models, improving biomedical data mining performance, and helping to prepare, clean, and understand data.

Original research papers are solicited in the theory behind dimension reduction as well as novel applications in bioinformatics, additional session topics include the following, including but not limited to the following topics.

• Dimensionality reduction
• Feature ranking
• Subset selection
• Feature extraction
• Feature construction
• Selection for labeled and unlabeled data
• Modeling variable and feature selection
• Goodness measures and evaluation
• Selection bias
• Selection with small samples
• Cross-discipline comparative studies
• Integration with data mining algorithms

Manuscripts should be submitted via the submission system of OSB08, which will be blindly reviewed by at least two reviewers, accepted papers will be published in Lecture Notes in Operations Research (LNOR) (Indexed by ISTP) on the conference, and selected papers will be invited to publish in IET Systems Biology (SCI, IF=1.955), International Journal of Artificial Intelligence and Soft Computing (IJAISC) and Journal of Computational Intelligence in Bioinformatics & System Biology (IJCIBSB) after this conference.

3. Network Reconstruction and Analysis

Chair: Yong Wang, Ph.D.
Assistant Professor of Institute of Applied Mathematics
Academy of Mathematics and Systems Science
Chinese Academy of Sciences, China
Tel: +86-10-62651362
Fax: +86-10-62561963
E-mail: ywang@amss.ac.cn

A leading trend in computational systems biology research is to understand cellular behavior in terms of interactions among genes, proteins and small molecules by introducing network concepts and data integration methodology. Recently, genomic and proteomic projects have produced a vast amount of biological sequence, structure, and interaction data. Study of the reconstruction of different kinds of biological networks by integration of heterogeneous data helps us understand the functionally relevant physical, genetic and metabolic interactions in a cellular network. At the same time the identification of condition-responsive sub-networks, pathways, and modules is of great importance in the network biology study. It is expected that the development of novel and accurate subnetwork identification models and algorithms will lead to more biological insights.

This special session attempts to promote a stronger interdisciplinary integration of expertise from researchers with different backgrounds. Hence, this session invites papers that apply computational methodologies to the following topics, but never exclusive:

• Gene Regulatory Network Reverse Engineering and Analysis
• Transcriptional Regulatory Network Reconstruction
• Metabolic Network or Signal Pathway Construction and Analysis
• Protein Interaction Network Prediction and Analysis
• Functional Linkage Network Reconstruction
• Disease Related Co-expression Network Analysis
• Integration Protein Interaction and Protein Structures Data
• Integration Protein Interaction Network and Gene Expression Data
• Heterogeneous Data Integration Methodology for Network Reconstruction
• Biological Pathway, Subnetwork, Module, Motif Identification and Analysis
• Other Relative Topics

4. Pattern Recognition in Bioinformatics

Chair: Xingming Zhao, Ph.D.
Associate Professor of Institute of Systems Biology
Shanghai University, Shanghai, China
Tel: +86-21-66136132
Fax: +86-21-66136129
E-mail: xm_zhao@shu.edu.cn

Pattern recognition is a popular technique in machine learning aiming at classification and prediction based on statistical information extracted from data. Recently, the pattern recognition approaches such as neural networks, SVM, genetic algorithms, etc, have been widely applied to analyze biological data. However, the accumulation of large amount of high-throughput data requires new pattern recognition methods, e.g. evolutionary algorithms, feature selection methods, classification algorithms, and so on. This session aims at bringing together researchers from different disciplines to discuss the applications of pattern recognition methods in the field of bioinformatics to solve problems in life sciences.

This session invites papers that utilize pattern recognition approaches for biological data analysis in bioinformatics, including but not limited to the following topics:

• Biological sequence analysis
• Gene expression analysis
• Prediction of protein secondary and tertiary structures
• Function prediction of proteins
• Protein complex prediction
• Molecular interaction prediction
• Identification of disordered regions in proteins
• Biomarker prediction
• Drug discovery