Special Session Call for Papers


Special Session


Diagnosis, Prognosis, and Survival Analysis of

Biopatterns using Machine Learning Methods

13-15 Dec. 2009, Miami, Florida, USA





A biopattern is the basic information (pattern) that provides clues about underlying clinical evidence for diagnosis and treatment of diseases. Typically, it is derived from specific data types, e.g. imaging, biometrics, genomics, proteomics, spectral signals (e.g., EEG, EKG), psychosocial, environmental, occupational, and family and personal history.  A biopattern is a personal “fingerprint” that fuses together a person’s current and past medical history, and future outcome.  It combines data, analysis, and predications of possible susceptibility to diseases.   Biopattern analysis aims to identify how biopatterns can be exploited for individualized healthcare.  Expert systems which incorporate pre-processing via distance learning metrics with flexible intelligent machine learning methods are amenable for diagnosis, prognosis, and survival analysis based on biopatterns.


This session will explore new machine learning methods developed for biopattern analysis in personalized medicine.  Areas of focus include biomedical diagnosis, prognosis, and survival analysis based on biopatterns.  Examples include novel methods for cluster analysis, distance metric learning, random forests, artificial neural networks, radial basis networks, hierarchical mixture models, genetic algorithms, etc.  This session will bring together leading researchers in machine learning, bioinformatics and medical informatics from academia, the healthcare sector and industry in a new way, harnessing expertise and information to rapidly thrust forward machine learning with biopatterns for expert systems and decision processing.  The contribution and public health impact of this special session is to advance new machine learning methods required for machine learning with biopatterns in personal medicine, namely, for the optimization of health care utilization based on care provider treatment preference and patient choice.


We encourage submission of papers on novel biopattern technologies focusing on diagnostic, prognostic, and survival modeling which incorporate machine learning algorithms including but not limited to:


·    Distance metric learning

·    Hebbian and neural adaptive learning

·    Artificial neural networks

·    Radial basis function networks

·    Hierarchical mixture models

·    Random forests


       and address technical issues including but not limited to:


·    Feature pre-processing

·    Cluster analysis

·    Image analysis

·    Electrophysiological signal processing

·    Integration of molecular data (genomic, proteomic) and clinical data

·    Function approximation, optimization, minimization

·    Classifier fusion

·    Decision support and expert systems



·    Computer scientists

·    Bioinformaticians

·    Nueroinformaticians

·    Medical informaticians

·    Statisticians

·    Molecular biologists

·    Biomedical and electrical engineers

·    Other researchers and developers



Paper Submission Deadline                      : August 1, 2009
Notification of acceptance                      : September 7, 2009
Camera-ready papers & Pre-registration    : October 1, 2009
The ICMLA Conference: December 13-15, 2009

All paper submissions will be handled electronically. Detailed instructions for submitting the papers are provided on the conference home page at




Papers submitted for this workshop should be submitted by July 15, 2009, at the regular paper submission website (http://www.icmla-conference.org/icmla09). Papers should not exceed a maximum of 6 pages (including abstract, body, tables, figures, and references), and should be submitted as a pdf in 2-column IEEE format. LaTeX document class files for 2-column format can be found on the IEEE Author Tools web page here: http://www.ieee.org/web/publications/authors/transjnl/index.html


Accepted papers will be published in the IEEE conference proceedings, as a hardcopy. All accepted papers must be presented by one of the authors to be published in the conference proceedings.


       Special Session Organizers:

·    Leif Peterson, Center for Biostatistics, The Methodist Hospital Research Institute, Houston, Texas 77030 USA, E-mail: lepeterson <at> tmh.tmc.edu

·    Paulo Lisboa, School of Computing Sciences, Liverpool John Moores University, Liverpool, UK  E-mail: P.J.Lisboa <at> ljmu.ac.uk


Program Committee Members:

·    Anne Denton, Department of Computer Science and Operations Research, North Dakota State Univ., USA

·    Alexandru Floares, Artificial Intelligence Department, Oncological Institute Klug-Napoca, Romania

·    Alexander Gelbukh, Center for Computing Research, National Polytechnic Institute, Mexico

·    José David Martín Guerrero, Dept. of Electrical Engineering, University of Valencia, Spain

·    Mark Kon, Department of Mathematics and Statistics, Boston University, USA

·    Tianming Liu, Computer Science Department, Univ. of Georgia, USA

·    Francesco Masulli, Department of Computer and Information Sciences, Univ. of Genova, Genoa, Italy

·    Mika Sato-Ilic, Department of Risk Engineering, Univ. of Tsubuka, Japan

·    Roberto Tagliaferri, Department of Mathematics and Computer Science, Univ. of Salerno, Italy