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.
We encourage papers that propose novel data mining techniques for areas including but not limited to :
Development of deep learning methods for biological and clinical data.
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 .
08月14日
2017
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