Manuscript Topics

The high throughput technologies in biomedicine, such as Next-Generation Sequencing (NGS), Mass Spectrum (MS) and imaging techniques, can generate huge amount of omics data in a short time. The structure of these data is complex. It is difficult to extract useful information or knowledge out of the big omics data. Furthermore, new technologies are emerging every day. There are great breakthroughs in single cell sequencing and genome editing.
On one way, we are facing great challenges in analyzing the big omics data; on the other way, we are having the opportunities to transform biomedicine and make medical practice with much more precision. The era of precision medicine has come because of recent advances in the big data analysis.
To overcome the great challenges in big data analysis and embrace precision medicine, we need more advanced mathematical and statistical methods. Fortunately, there are huge success stories of new computing algorithms, such as deep learning and new hardware, such as GPU (Graphics Processing Unit). With these powerful tools, we are able to process much more complex data and spend much less time to get the results.
We would like to propose a special issue for "Mathematical Biosciences and Engineering" to introduce the latest developments of big data analysis for precision medicine.

Potential topics include, but not limited to:

Deep Learning in Health Sciences
Disease Gene Identification
Multi-omics Integration
Network Structure Analysis
Mathematical Modelling of Disease Process
Driver Gene Identification
Tumour Heterogeneity Investigation
Single Cell Analysis
Genome Editing
Off-target Analysis of CRISPR/Cas9

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