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

Frank Emmert-Streib, Shu Dong Zhang, Peter Hamilton

    Research output: Contribution to journalArticleScientificpeer-review

    4 Citations (Scopus)


    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
    JournalBMC Cancer
    Issue number1
    Publication statusPublished - 15 Jan 2015
    Publication typeA1 Journal article-refereed


    • Cancer
    • Computational biology
    • Computational genomics
    • Computational oncology
    • Genomics data
    • Statistical genomics
    • Systems medicine

    ASJC Scopus subject areas

    • Oncology
    • Cancer Research
    • Genetics


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