Abstract
Background: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner. Discussion: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research. Summary: Here we argue that this imbalance, favoring 'wet lab-based activities', will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization.
Original language | English |
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Article number | 7 |
Journal | BMC Cancer |
Volume | 15 |
Issue number | 1 |
DOIs | |
Publication status | Published - 15 Jan 2015 |
Publication type | A1 Journal article-refereed |
Keywords
- Cancer
- Computational biology
- Computational genomics
- Computational oncology
- Genomics data
- Statistical genomics
- Systems medicine
ASJC Scopus subject areas
- Oncology
- Cancer Research
- Genetics