Title |
Computational cancer biology: education is a natural key to many locks
|
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Published in |
BMC Cancer, January 2015
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DOI | 10.1186/s12885-014-1002-2 |
Pubmed ID | |
Authors |
Frank Emmert-Streib, Shu-Dong Zhang, Peter Hamilton |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 29% |
United Kingdom | 2 | 14% |
Germany | 1 | 7% |
Spain | 1 | 7% |
France | 1 | 7% |
Switzerland | 1 | 7% |
Unknown | 4 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 57% |
Scientists | 5 | 36% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 2% |
Chile | 1 | 2% |
Canada | 1 | 2% |
Unknown | 46 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 7 | 14% |
Researcher | 6 | 12% |
Student > Ph. D. Student | 5 | 10% |
Student > Bachelor | 5 | 10% |
Student > Postgraduate | 4 | 8% |
Other | 12 | 24% |
Unknown | 10 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 7 | 14% |
Agricultural and Biological Sciences | 5 | 10% |
Biochemistry, Genetics and Molecular Biology | 5 | 10% |
Engineering | 5 | 10% |
Computer Science | 4 | 8% |
Other | 12 | 24% |
Unknown | 11 | 22% |