Adapting bioinformatics curricula for big data
Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are ch...
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| Vydáno v: | Briefings in bioinformatics Ročník 17; číslo 1; s. 43 - 50 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Oxford Publishing Limited (England)
01.01.2016
Oxford University Press |
| Témata: | |
| ISSN: | 1467-5463, 1477-4054, 1477-4054 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. |
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| Bibliografie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-2 ObjectType-Feature-2 |
| ISSN: | 1467-5463 1477-4054 1477-4054 |
| DOI: | 10.1093/bib/bbv018 |