One of the grand challenges in our networked world are the large, complex, and often weakly structured data sets along with the massive amounts of unstructered information. This "big data" challenge (V4 Challenge: Volume, Variety, Velocity, Veracity) is most evident in the biomedical domain: the trend towards personalized medicine (P4 Medicine: Predictive, Preventative, Participatory, Personalized) has resulted in an explosion in the amount of generated biomedical data sets- in particular Omics data (e.g. from genomics, proteomics, metabolomics, lipidomics, transcriptomics, epigenetics, microbiomics, fluxomics, phenomics, etc.). This supplement is a special collection of 7 peer reviewed articles carefully selected to provide an overview of this novel, emerging and important new area.
Volume 15 Supplement 6
Knowledge Discovery and Interactive Data Mining in Bioinformatics
Research
Edited by Andreas Holzinger, Matthias Dehmer and Igor Jurisica
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. Articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
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Citation: BMC Bioinformatics 2014 15(Suppl 6):I1
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Biochemical systems identification by a random drift particle swarm optimization approach
Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signa...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S1 -
Selection of entropy-measure parameters for knowledge discovery in heart rate variability data
Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multipl...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S2 -
Data-driven discovery of seasonally linked diseases from an Electronic Health Records system
Patterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patt...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S3 -
Furby: fuzzy force-directed bicluster visualization
Cluster analysis is widely used to discover patterns in multi-dimensional data. Clustered heatmaps are the standard technique for visualizing one-way and two-way clustering results. In clustered heatmaps, rows...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S4 -
Analysis of biomedical data with multilevel glyphs
This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple leve...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S5 -
Functional and genetic analysis of the colon cancer network
Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into accou...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S6 -
Knowledge discovery of drug data on the example of adverse reaction prediction
Antibiotics are the widely prescribed drugs for children and most likely to be related with adverse reactions. Record on adverse reactions and allergies from antibiotics considerably affect the prescription ch...
Citation: BMC Bioinformatics 2014 15(Suppl 6):S7
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