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 |
|---|---|
| Article number | 7 |
| Number of pages | 6 |
| Journal | BMC Cancer |
| Volume | 15 |
| DOIs | |
| Publication status | Published - 15 Jan 2015 |
Keywords
- Cancer
- Computational biology
- Genomics data
- Computational oncology
- Computational genomics
- Statistical genomics
- Systems medicine
- BIG DATA
- EXPRESSION
- CLASSIFICATION
- SUBCLASSES
- CARCINOMAS
Access to Document
- Computational cancer biology: education is a natural key to many locks
© 2015 Emmert-Streib et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.