BIOKDD 2015 - 14th International Workshop on Data Mining in Bioinformatics (BIOKDD)

Bioinformatics is the science of managing, mining, and interpreting information from biological data. Various genome projects have contributed to an exponential growth in DNA and protein sequence databases. Rapid advances in high-throughput technologies, such as microarrays, mass spectrometry and new/next-generation sequencing, can monitor quantitatively the presence or activity of thousands of genes, RNAs, proteins, metabolites, and compounds in a given biological state. The ongoing influx of these data, the pressing need to address complex biomedical challenges, and the gap between the two have collectively created exciting opportunities for data mining researchers.
While tremendous progress has been made over the years, many of the fundamental problems in bioinformatics, such as protein structure prediction, gene-environment interaction, and regulatory network mapping, have not been convincingly addressed. Besides these, new technologies such as next-generation sequencing are now producing massive amounts of sequence data; managing, mining and compressing these data raise challenging issues. Finally, there is a pressing need to use these data coupled with efficient and effective computational techniques to build models of complex biological processes and disease phenotypes. Data mining will play an essential role in addressing these fundamental problems and in the development of novel therapeutic/diagnostic/prognostic solutions in the post-genomics era of medicine.
The goal of this workshop is to encourage KDD researchers to take on the numerous challenges that Bioinformatics offers. This year, the workshop will feature the theme of “Knowledge Discovery on Complex Biological and Medical Data”. This field focuses on the use of data mining and machine learning approaches for the analysis of the large amount of heterogeneous complex biological and medical data being generated. The goal here is to build accurate predictive or descriptive models from these data enabling novel discoveries in basic biology and medicine.
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