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. 2018 Jul 1;34(13):i4-i12.
doi: 10.1093/bioinformatics/bty233.

Training for Translation Between Disciplines: A Philosophy for Life and Data Sciences Curricula

Free PMC article

Training for Translation Between Disciplines: A Philosophy for Life and Data Sciences Curricula

K Anton Feenstra et al. Bioinformatics. .
Free PMC article


Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other.

Results: Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula.

Supplementary information: Supplementary data are available at Bioinformatics online.


Fig. 1.
Fig. 1.
Conceptual organization of bioinformatics and systems biology education along three key elements: balance, translate and focus
Fig. 2.
Fig. 2.
Diversity of incoming students by topic area, over six cohorts from 2011 to 2016; the vertical axis shows fraction of students. Counting individual programme topics, bioinformatics bachelors are the largest fraction (here counted with together ‘biotech’), but on average bioinformaticians account for only 12% of all students. In recent years, biomedical bachelors are starting to dominate the influx, recently accounting for about one-third of new students
Fig. 3.
Fig. 3.
Correlation between grades in the maths and programming classes, and subsequent course exam grades, based on 2015–2016 grades. A—Grades of the maths class correlate well with exam grades of fundamentals of bioinformatics (FoB) and introduction to systems biology (ISB). B—Correlation of maths grades with bioinformatics courses, FoB and the later structural bioinformatics course, is also high. C—For the programming class grades, this correlation is much lower, here shown for FoB and ISB

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