Computational cancer biology: education is a natural key to many locks

Frank Emmert-Streib*, Shu-Dong Zhang, Peter Hamilton

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)
264 Downloads (Pure)

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 languageEnglish
Article number7
Number of pages6
JournalBMC Cancer
Volume15
DOIs
Publication statusPublished - 15 Jan 2015

Keywords

  • Cancer
  • Computational biology
  • Genomics data
  • Computational oncology
  • Computational genomics
  • Statistical genomics
  • Systems medicine
  • BIG DATA
  • EXPRESSION
  • CLASSIFICATION
  • SUBCLASSES
  • CARCINOMAS

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