Computational cancer biology: education is a natural key to many locksReport as inadecuate

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BMC Cancer

, 15:7

Systems biology, post-genomic analysis and emerging technologies


BackgroundOncology 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.

DiscussionFor 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.

SummaryHere 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.

KeywordsCancer Computational biology Genomics data Computational oncology Computational genomics Statistical genomics Systems medicine  Download fulltext PDF

Author: Frank Emmert-Streib - Shu-Dong Zhang - Peter Hamilton


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