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

    6 Citations (Scopus)

    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
    JournalBMC Cancer
    Volume15
    Issue number1
    DOIs
    Publication statusPublished - 15 Jan 2015
    Publication typeA1 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

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