Special Session

on

Statistical Data Mining and Machine Learning in Cancer Epidemiology and Cancer Bioinformatics

Of

The Seventh International Conference on Machine Learning and Applications (ICMLA’ 08)

http://www.icmla-conference.org/icmla08/

December 11-13, 2008 – San Diego, California, USA

 

Motivation

High throughput technologies such as microarray have produced huge amount of data in public domain. Many survey and clinical outcome data such as SEER data are also available.  A long list of links to large health-related data sets can be found at the website http://www.ehdp.com/vitalnet/datasets.htm. All of these databases have different temporal and spatial assumptions (for example, different frequencies of collection, different spatial resolution (by state, by county, by zip-code, by square kilometer), etc. How to mine these data together and extract useful information is really a challenging task. Although we have seen many applications of data mining and machine learning techniques in microarray and other high throughput data, there are much less applications in SNP array and cancer epidemiology.  It is the organizer’s belief that new computational methods are needed to deal with large, complex data sets arising in cancer epidemiology and cancer bioinformatics. We see a pressing need for and benefits in the interdisciplinary exchange and discussion of ideas. We anticipate that this workshop will shed light on research directions and provide the stimulus for creative breakthroughs.

 

Themes

This special session will bring together researchers from different disciplines and encourage collaborative research on cancer related data mining. The objectives of this workshop are intended to addressing two challenging issues.  One is how to identify and evaluate biomarkers (features, risk/protector) factors. The other is to develop new or adapt existing algorithms to analyze data from different sources.

 

Important Date

 

Regular Research Papers due                           June 15, 2008   July 15, 2008

Notification of acceptance                                           September 1, 2008

Camera-ready papers & Pre-registration                      October 1, 2008

The ICMLA Conference                                              December 11-13, 2008

 

Topics

Original research papers in the theory in data mining and machine learning with emphasis of applications in cancer genetics, cancer bioinformatics, and cancer epidemiology are solicited.  Specific topics include but not limited to:

 Feature Selection and Biomarker Evaluations

Data Mining and Machine Learning in SNP Tagging and Genomewide Association Studies

Mining Data from Different Sources

 

Paper Submission:

Please submit a full length paper through the online submission system. Electronic submission is required. Authors will be notified of the acceptance after the review process by two independent reviewers. All papers accepted will be included in the Workshop Proceedings published by the IEEE Computer Society Press and will be available at the workshop.

 

 

 

If you have further questions, please contact the program chairs zliu@umm.edu or ysong@umes.edu.

 

 

Organization:

 

Special Session Chairs:

Zhenqiu Liu:         zliu@umm.edu

University of Maryland School of Medicine, USA

 

Yinglei Song: ysong@umes.edu

University of Maryland Eastern Shore, USA

 

Program Committee:

 

Halima Bensmail, University of Tennessee, Knoxville, USA

 

 Dong Xu, University of Missouri, USA

 

 Shili Lin, The Ohio State University, USA

 

  Dechang Chen, Uniformed Services University of the Health Sciences, USA

 

Haomiao Jia, Columbia University, USA.

 

  Ming Tan, University of Maryland, USA

 

Babis Papachristou, University of Chicago, USA      

 

Yunhu Wan, University of Maryland,  USA

 

JianJun (Paul) Tian, College of William and Mary, USA                        

 

Li Sheng, Drexel University, USA   

 

Wei Guo, The Ohio State University, USA

 

Russell L. Malmberg, University of Georgia, USA

 

Mark Kon, Boston University, USA

 

Chunmei Liu, Howard University, USA

 

Junfeng Qu, Clayton State University, USA