A situation that is observed now in the study of cancer-related problems can be described as a “collapse of understanding.” Recently developed techniques such as genome sequencing, measurement of multiple oncomarkers, DNA methylation profile, genomic profile, or transcriptome of the pathological tissue provide huge amounts of data. Despite significant progress in understanding many of the research questions related to cancer, we are still far away from solving the problem of cancer. One of the reasons for this is that at the moment there is more data available than can be practically analysed. Besides just having a large amount, this data is also very heterogeneous in terms of the type of information it contains (qualitative, quantitative, and verbal description), which further complicates its analysis and requires multiscale and sophisticated analysis.

Solution to the problem of analysis of cancer data requires using modern and sophisticated methods of data analysis developed in systems biology and cybernetics and explaining the effects by modeling of underlying genetic networks, but the gap between the communities of clinicians and applied mathematicians remains large and hinders a full-scale application of advances in data analysis. The situation is complicated even further when one tries to connect modelling of the onset and development of cancer with real clinical data, or to use the discoveries of nonlinear dynamics to explain oncopathologies.

For this special issue, we strongly welcome reviews addressing very broad scale and basic level hypotheses underlying current cancer research, papers describing data analysis studies, development of machine-learning algorithms, and modelling complex gene-regulating dynamics with the aim of bringing together different communities, such as applied mathematicians and physicists developing new methods for data analysis and modelling, and applied scientists working together with biologists and clinicians.

Potential topics include, but are not limited to:

• Data mining, screening, and processing of cancer data
• Description of different data analysis methodologies, multiscale modelling, big data analysis, and incorporation of various heterogeneous information
• Analysis of multiple and longitudinal oncomarkers for early diagnosis
• Studies of epigenetic data (e.g., DNA methylation profiles) and its corruption in cancer
• Modelling of genetic regulatory networks and cell signalling networks in cancer
• Design of synthetic genetic networks potentially useful for clinical applications
• Methodologies for modelling cancer development processes