Workshop on Machine Learning in Life Sciences

Scope of the Workshop

Life sciences, ranging from medicine, biology and genetics to biochemistry and pharmacology have developed rapidly in previous years. Computerization of those domains allowed to gather and store enormous collections of data. Analysis of such vast amounts of information without any support is impossible for human being. Therefore recently machine learning and pattern recognition methods have attracted the atte ntion of broad spectrum of experts from life sciences domain.
 
The aim of this Workshop is to stress the importance of interdisciplinary collaboration between life and computer sciences and to provide an international forum for both practitioners seeking new cutting-edge tools for solving their domain problems and theoreticians seeking interesting and real- life applications for their novel algorithms. We are interested in novel machine learning technologies, designed to tackle complex medical, biological, chemical or environmental data that take into consideration the specific background knowledge and interactions between the considered problems. We look for novel applications of machine learning and pattern recognition tools to contemporary life sciences problems, that will shed light on their strengths and weaknesses. We are interested in new methods for data visualization and methods for accessible presentation of results of machine learning analysis to life scientists. We welcome new findings in the intelligent processing of non-stationary medical, biological and chemical data and in proposals for efficient fusion of information coming from multiple sources. Papers on efficient analysis and classification of bid data (understood as both massive volumes and high-dimensionality problems) will be of special interest to this Workshop.The scope of this Workshop focuses mainly on, but is not limited to:
  • novel machine learning and pattern recognition models for analyzing medical, biological and chemical data;
  • new models for efficient, fast and effective processing of big, massive and multi-dimensional life sciences data;
  • intelligent methods for analysis of microarrays, gene and protein modeling, biological networks, docking etc;
  • automatic drug design with machine learning methods, QSAR / QSPR modeling;
  • methods for data visualization and accessible presentation of machine learning results / findings to domain experts (doctors, biologists, chemists etc);
  • new developments in ensemble, compound and hybrid classification for life sciences;
  • methods for incorporating background knowledge into machine learning systems;
  • recent developments in complex data pre-processing: feature selection, noise filtering, class imbalance etc;

Workshop Organizers

Bartosz Krawczyk, M.Sc. Eng.

Department of Systems and Computer Networks, Faculty of Electronics
Wroclaw University of Technology
 
 

Prof. Michal Wozniak, Ph.D., D.Sc.

Department of Systems and Computer Networks, Faculty of Electronics
Wroclaw University of Technology