DMLS 2014 - International Workshop on Data Mining in the Life Sciences

Data mining in biology and medicine is a core component of biomedical informatics, and one of the first intensive applications of computer science to this field, whether at the clinic, the laboratory, or the research center. Following a long tradition of data exploration stemming from biostatistical data analysis, todays's biomedical data mining appears more multifaceted with advances in knowledge discovery in databases as well as machine learning approaches.
The goals of this workshop are to:
provide a forum for identifying important contributions and opportunities for research on data mining as it applies to biological and/or medical data,
promote the systematic study of how to apply data mining to biology and medicine, and
show case applications of data mining in biology and medicine.
Some of the technical issues addressed, and potential outcomes of the workshop, are to identify preferred types of mining methods, tools, and processes, preferred domains of application, how to connect a data mining model with a problem to solve, challenges specific to applying data mining to biology and medicine, and guidelines to better develop data mining projects in this domain. We welcome all those interested in the problems and promise of data mining in biology or medicine as well as in bioinformatics, Human Genome Project, environmental sciences and agriculture.
Topics of interest include (but are not limited to):
With regard to different types of data:
Discovery of high-level structures, including e.g. association networks
Text mining from biomedical literatur
Medical images mining
Biomedical signals mining
Temporal and sequential data mining
Mining heterogeneous data
Mining data from molecular biology, genomics, proteomics, pylogenetic classification
With regard to different methodologies and case studies:
Data mining project development methodology for biomedicine
Integration of data mining in the clinic
Ontology-driver data mining in life sciences
Methodology for mining complex data, e.g. a combination of laboratory test results, images, signals, genomic and proteomic samples
Data mining for personal disease management
Utility considerations in DMLS, including e.g. cost-sensitive learning
We particularly welcome case studies and applications and discussions of the lessons learned from such case studies