Integrating large-scale data obtained at multiscales is essential for understanding the molecular basis of complex diseases and providing useful therapeutic targets. Several large projects, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression (GTEx), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), have measured the somatic mutation, copy number variation (CNV), mRNA expression, microRNA expression, and methylation data and made them publicly available. Recently, many statistical methods and analysis tools have been developed based on these multiscale large-scale data.

The aim of this Special Issue is to collate original research and review articles from oncologists, computational biologists, mathematical biologists, bioinformaticians, systems biologists, and computational pharmacists with a focus on these statistical methods and analysis tools.

Potential topics include but are not limited to the following:

• Subtype stratification of patients
• Multiscale network construction
• Network module identification
• Predictive model of disease state
• Biomarker discovery
• Combinatorial drug discovery
• Translational medicine
• Novel computational methods in large biology data analysis
• Inferring gene function from expression data
• Inferring gene function from genome sequence data
• Integrating expression data with other genome-wide data for functional annotation
• Integrating expression data from different organisms