BIOKDD 2014 - 13th International Workshop on Data Mining in Bioinformatics (BIOKDD’14)

Data Mining approaches are ideally suited for Bioinformatics, since it is data-rich, but lacks a comprehensive theory of life's organization at the molecular level. The extensive databases of biological information create both challenges and opportunities for developing novel KDD methods.
This year, the workshop will feature the theme of "Knowledge discovery using big data in biological/biomedical systems". This field focuses on the use of computational and statistical approaches, especially from data mining and machine learning, and the large amount and variety of biological data being generated. The goal is to build accurate predictive or descriptive models of biological processes and diseases or in integrating data/knowledge-bases from diverse sources to provide experimentally testable hypothesis. These approaches can revolutionize new age biology by enabling novel discoveries in basic biology and diseases like cancer and diabetes, as well as the development of therapeutics.
We encourage papers that propose novel data mining techniques for areas including but not limited to:
Building predictive models for complex phenotypes from large-scale biological data
Discovering biological networks and pathways underlying biological processes and diseases
Processing of new/next-generation sequencing (NGS) data for genome structural variation analysis, discovery of biomarkers and mutations, and disease risk assessment
Discovery of genotype-phenotype associations
Novel methods and frameworks for mining and integrating big biological data
Comparative genomics
Metagenome analysis using sequencing data
RNA-seq and microarray-based gene expression analysis
Genome-wide analysis of non-coding RNAs
Genome-wide regulatory motif discovery
Structural bioinformatics
Correlating NGS with proteomics data analysis
Functional annotation of genes and proteins
Cheminformatics
Special biological data management techniques
Information visualization techniques for biological data
Semantic web and ontology-driven data integration methods
Privacy and security issues in mining genomic databases