IEEE Symposium Series on Computational Intelligence 2009

2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

The 6th Annual IEEE CIBCB symposium will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in Bioinformatics and Computational Biology. Computational Intelligence includes artificial neural nets, fuzzy logic, evolutionary algorithms, and other emerging techniques such as particle swarm optimization. Topics of interest include but are not limited to:

• Protein and RNA Structure Prediction, Folding, and Docking
• Gene Finding and RNA Gene Finding
• Gene Expression Array Analysis
• Molecular Sequence Alignment and Analysis
• Phylogenetic Analysis
• Analysis of Very Large Biological Data Sets
• Drug Design
• Metabolic Pathway Analysis
• Medical Imaging and Pattern Recognition

The use of computational intelligence must play a substantial role in submitted papers. Submissions will be vigorously peer reviewed by at least 2 experts in the field and accepted papers will be published in the conference proceedings.

Call for Special Session Proposals at IEEE CIBCB 2009

Proposals for specials sessions within IEEE CIBCB 2009 are sought which address a specific application area within bioinformatics and computational biology or which focus on a specific computational intelligence methodology.
As is the case for the larger IEEE CIBCB 2009 conference, papers within special sessions are expected to contain both computational intelligence AND bioinformatics/computational biology content. Those who wish to submit a proposal for a Special Session, should submit by August 31, 2008 to Scott Smith, Special Sessions Chair for CIBCB (sfsmith-AT-boisestate-DOT-edu). Proposals should include a title, name of chair(s), and the scope of the session. A brief CV for each proposed chair should be included as well.

Accepted Special Sessions:

Invited Special Session on CyberInfrastructure and Computational Intelligence for Population Health and Behavioral Sciences

Theme and scope:

Recent advances in the fields of population health and behavioral sciences have resulted in a dramatic increase in data. Because of this trend, interest towards the investigation of computational approaches that can rapidly analyze and identify patterns in health-related data across levels from the genome to the population also continues to grow. The National Institutes of Health offers programs such as the Genes, Environment and Health Initiative (GEI) to support research that will lead to the understanding of genetic contributions and gene-environment interactions in common disease. In addition, programs such as the National Cancer Institute's cancer Biomedical Informatics Grid (caBIG) are utilizing grid-based computing to help support a transformative science in behavioral medicine through encouragement of data sharing, development of standardized data elements, greater computational power, and the integration of geo-spatial, psychosocial, and population health surveillance data geared towards systems science and machine learning approaches for data mining. The National Science Foundation and other Federal agencies are also playing a key role towards identifying technologies that can seamlessly utilize the current and developing cyberinfrastructure for scientific research and applications. Computational intelligence algorithms including artificial neural networks, evolutionary computation, fuzzy systems, and other emerging techniques represent a key aspect of this transition to large database mining in the health and behavioral sciences. This invited special session will introduce the audience to research topics, problems and datasets in the fields of population health and behavioral science and also provide a discussion of possible funding avenues through NIH and NSF for novel and high-impact applications of computational intelligence in these areas.

The invited special session will include three lectures by specialists from the NIH and NSF and include a panel discussion for audience participation. The panel will be moderated by Dr. Gary Fogel. Abstracts of the lectures are provided below.

CI2: CyberInfrastructure and Computational Intelligence: Recent Experience and Current Activities at NSF in Fostering Transformative Cyber-Enabled Research

Fahmida Chowdhury, Ph.D., Program Director, Cross-Directorate Activities Social, Behavioral and Economic Sciences (SBE), National Science Foundation

Edward Seidel, Ph.D., Director, Office of Cyberinfrastructure (OCI), National Science Foundation

Abstract: Although originally used in a more restricted sense, the term Cyberinfrastructure (CI) has come to represent the coordinated aggregate of software, hardware, Internet and other communications technologies, as well as human expertise used for scientific research. (This “CI” is of course quite different than the “CI” of computational intelligence.) This presentation will describe recent activities at NSF involving CyberInfrastructure: in particular, the process of cultural transformation that has taken place within the NSF-supported fields of science and engineering. As a high-profile, Foundation-wide endeavor, CyberInfrastructure has played the role of an enabler and attractor for the collaboration of various disciplines via the sharing of data, resources, networks, and scientific skill. It can be argued that NSF has, to a certain extent, engineered this transformation by requiring truly interdisciplinary teams of principal and co-principal investigators in many of its grant solicitations. This presentation will focus on the currently available CyberInfrastructure-related funding opportunities at NSF that should be attractive for the computational intelligence community. This provides a great opportunity for creating innovative multi-disciplinary teams involving both kinds of “CI” for transformative research in bioinformatics and other disciplines.

Transforming Behavioral Medicine: Cyberinfrastructure in Cancer Prevention and Control

Abdul R. Shaikh, Ph.D., MHSc, Behavioral Scientist, Program Director, Health Communication and Informatics Research, National Cancer Institute, National Institutes of Health

Bradford W. Hesse, Ph.D., Branch Chief, Health Communication and Informatics Research, National Cancer Institute, National Institutes of Health

Abstract: Advances in health information technology (HIT) and grid-based computing are transforming health-related research and practice. The National Cancer Institute (NCI) is focused on identifying technologies and computational approaches that can rapidly analyze and identify patterns in health-related data spanning “cells to society.” Cyberinfrastructure (CI), or grid-based computing and the human and technical infrastructure it supports has the potential to support a new, transformative science in behavioral medicine by supporting collaboration, encouraging standardized data elements and data sharing, providing greater computational power, and the ability to run cutting-edge analyses. This presentation will use cancer-related research to illustrate the current challenges and potential advantages of cyber-enabled research in behavioral medicine, including multi-disciplinary collaboration, sharing of common measures and data, integration of disparate datasets, and new analytic approaches that can rapidly analyze and identify multi-level patterns in health data. Potential NCI funding opportunities in cyber-enabled research will also be discussed.

A Role for Systems Science Methodologies in Addressing Complexity in Population Health

Patricia L. Mabry, Ph.D. Office of Behavioral and Social Sciences Research, National Institutes of Health

Stephane Philogene, Ph.D., Office of Behavioral and Social Sciences Research, National Institutes of Health

Abstract: Approximately 50% of premature deaths and 70% of chronic illnesses in U.S. could be prevented by changing behavioral risk factors and the related social and physical systems necessary to achieve and sustain those changes. There is a growing recognition that the behavioral and social aspects of health and disease are the very things make these problems so dynamically and relationally complex, and hence intractable. Cardiovascular disease, pulmonary disease, cancer, diabetes, mental health problems, HIV/AIDS, substance abuse, violence, emerging infectious diseases, obesity, sedentary lifestyle, poor diet, sleep disorders, are but a few examples. Each is complex in the sense that it arises from an intricate mix of behavioral, economic, and social factors interacting with biological factors, as well as each other, over the lifespan and across an array of settings (e.g., home, school, workplace, neighborhood, etc.). Traditional approaches to addressing these problems include correlation-based analytic methods (e.g., regression), and reductionist approaches that seek to reduce problems to their simpler or more fundamental components. Over-reliance on these methods for many decades has failed to resolve our health problems and may have even hindered our progress by diverting resources to a never-ending search for more piecemeal data. We are now armed with a plethora of existing data, but only a small amount of research has attempted to synthesize this data into a meaningful and actionable whole using systems science methodologies. Because they specialize in detecting non-linear relationships, finding time delayed effects, and identifying bidirectional relationships, they are much better equipped to help us understand the “big picture” perspective of problems. In this talk, we describe some of the methodological roadblocks encountered by behavioral and social scientists in their research on health and disease. Case examples will be used to illustrate how some systems science methodologies have already begun to be used to address specific public health challenges. Finally, current funding opportunity announcements from the National Institutes of Health that encourage systems science approaches will be presented.

Co-sponsored by the Bioinformatics and Bioengineering Technical Committee and the Women in Computational Intelligence Technical Committee of the IEEE Computational Intelligence Society

Special Session Co-chairs:

Gary B. Fogel, Ph.D., Natural Selection, Inc., gfogel[at]natural-selection.com
Fahmida Chowdhury, Ph.D., National Science Foundation, fchowdhu[at]nsf.gov

Program Chair:
Kay C. Wiese, Simon Fraser University, Canada

Publicity Co-Chair for CIBCB:
Gary Fogel, Natural Selection Inc., USA

Special Sessions Chair for CIBCB:
Scott F. Smith, Boise State University, USA

Program Committee:
Daniel Ashlock, University of Guelph, Canada
Sanghamitra Bandyopadhyay, Indian Statistical Institute, India
Chengpeng "Charlie" Bi, University of Missouri , USA
Fiona Browne, University of Ulster, UK
Jacek Blazewicz, Poznan University of Technology, Poland
David Cairns, University of Stirling, UK
Xue-wen Chen, The University of Kansas, USA
Madhu Chetty, Monash University, Australia
Clare Bates Congdon, University of Southern Maine, USA
David Corne, Heriot-Watt University, UK
Steven Corns, Missouri University of Science and Engineering, USA
Gary Fogel, Natural Selection, Inc, USA
Christoph Friedrich, Fraunhofer Institute, Germany
Saman Halgamuge, University of Melbourne, Australia
Jennifer Hallinan, Newcastle University, UK
Lutz Hamel, University of Rhode Island, USA
Andrew Hendriks, Simon Fraser University, Canada
David Hecht, Southwestern College California, USA.
Daniel Howard, QinetiQ Inc., UK
De-Shuang Huang, Chinese Academy of Sciences, China
Christian Jacob, University of Calgary, Canada
Elena Marchiori, Radboud University, Netherlands
Alioune Ngom, University of Windsor, Canada
Carlos Penha, Vaud University of Applied Sciences, Switzerland
Mihail Popescu, University of Missouri, USA
Jagath Rajapakse, NTU, Singapore
Jem Rowland, University of Wales Aberystwyth, UK
Scott F. Smith, Boise State University, USA
Rene Thomsen, Molegro ApS, Denmark
Herbert Tsang, Simon Fraser University, Canada
L. Gwenn Volkert, Kent State University, USA
Anil Wipat, University of Newcastle, UK
Yanqing Zhang, Georgia State University, USA