New Challenges in Integrative Biomedical Data Analysis

Dear Colleagues,

The recent tremendous accumulation of biomedical data offers challenges and opportunities to understand biological phenomena and disease. In particular, biomedical data have been generated from multiple perspectives such as the genome, transcriptome, proteome, epigenome, microbiome, clinical information including electronic health records, lifestyle, and so on. In many cases, however, biomedical phenomena cannot be understood by a single data type. For instance, molecular profiling data can be much more helpful for precision medicine when it has been analyzed in a clinical setting. Moreover, integrative analysis of multi-omics data can help us to understand the complex biological systems and derive insights into cellular functions.

Thus, the development of methods to combine and analyze multiple biomedical data is one of the key challenges in the biomedical domain. Comprehensive and relevant analysis will answer many biomedical questions by overcoming the limitations of singular data.

This Special Issue welcomes both review papers and original research works for integrative biomedical data analysis, along with their applications. The topics include (but are not limited to) the following:

• Biomedical discovery using multi-omics data;
• Data analysis based on statistics/machine learning/data mining;
• Pharmacogenomics, drug repositioning, computational drug discovery;
• Radiogenomics;
• Translational medicine;
• High-performance computing;
• Time-course data analysis;
• Data preprocessing and cleaning;
• Meta-analysis in biomedical research;
• Standards for biomedical/clinical/healthcare data.

Prof. Dr. Je-Keun Rhee
Guest Editor

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