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- Research interest:Both biological sciences and clinical medicine are currently overwhelmed by vast amounts of complex data and are becoming increasingly dependent on information technology for data analysis, interpretation and organisation. Although powerful data mining techniques are being developed within and outside the University, they are still underutilized in the life sciences. We aim to bring state-of-the-art data mining techniques to life science applications by setting up interdisciplinary collaborations between computer scientists, life scientists and clinicians. Core activities are:1) the introduction, adaptation and application of innovative pattern mining and machine learning techniques to heterogeneous 'omics data (genome, transcriptome, proteome and metabolome) and to clinical information; 2) using these techniques to generate computational (network) models for biological systems and diseases; and3) the development of interactive and intuitive visualizations of complex life science data and pattern mining results.
- Keywords:PATTERN MINING, DATA MINING, SYSTEMS MEDICINE, COMPUTATIONAL BIOLOGY, SYSTEMS BIOLOGY, BIOINFORMATICS, ARTIFICIAL INTELLIGENCE, Biology
- Disciplines:Data mining, Scientific computing, Bioinformatics and computational biology, Bioinformatics data integration and network biology, Computational biomodelling and machine learning, Bioinformatics of disease, Public health care, Public health services, Analysis of next-generation sequence data
- Research techniques:Various bioinformatics, data mining and artificial intelligence techniques: analysis, interpretation, pattern mining, integration of heterogeneous 'omics data (genome, transcriptome, proteome, metabolome), sequence and structure analysis, spectrum analysis and interpretation, functional analysis of molecular data, multivariate statistics, network theory, algorithmic modeling, advanced machine learning, deep learning, advanced data visualization, prototyping
- Users of research expertise:Life scientists, biomedical researchers, clinicians, molecular biologists