Computational cancer biology: education is a natural key to many locks
Data(s) |
15/01/2015
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Resumo |
<p>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.</p><p>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.</p><p>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.</p> |
Formato |
application/pdf |
Identificador |
http://dx.doi.org/10.1186/s12885-014-1002-2 http://pure.qub.ac.uk/ws/files/14442325/Computational_cancer_biology.pdf |
Idioma(s) |
eng |
Direitos |
info:eu-repo/semantics/openAccess |
Fonte |
Emmert-Streib , F , Zhang , S-D & Hamilton , P 2015 , ' Computational cancer biology: education is a natural key to many locks ' BMC Cancer , vol 15 , 7 . DOI: 10.1186/s12885-014-1002-2 |
Palavras-Chave | #Cancer #Computational biology #Genomics data #Computational oncology #Computational genomics #Statistical genomics #Systems medicine #BIG DATA #EXPRESSION #CLASSIFICATION #SUBCLASSES #CARCINOMAS |
Tipo |
article |