ReviewBriefings in bioinformatics2016
Adapting bioinformatics curricula for big data.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Managing the Transition from Tradition to Innovation for the Heidelberg/Heilbronn Medical Informatics Master of Science Program.Applied clinical informatics · 2025Article
- Unveiling the role of miR-137-3p/miR-296-5p/SERPINA3 signaling in colorectal cancer progression: integrative analysis of gene expression profiles and in vitro studies.BMC medical genomics · 2023Article
- Genomics of psychiatric disorders: Regional challenges and opportunitiesBiomedica : revista del Instituto Nacional de Salud · 2023Article
- Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks.NAR genomics and bioinformatics · 2021Article
- Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource.PloS one · 2021Article
- How to balance the bioinformatics data: pseudo-negative sampling.BMC bioinformatics · 2019Article
- Challenges and recommendations to improve the installability and archival stability of omics computational tools.PLoS biology · 2019Article
- Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors.PLoS computational biology · 2019Article
- A Bioinformatics View of Glycan⁻Virus Interactions.Viruses · 2019Review
- Training for translation between disciplines: a philosophy for life and data sciences curricula.Bioinformatics (Oxford, England) · 2018Article
- Big Data in Public Health: Terminology, Machine Learning, and Privacy.Annual review of public health · 2018Review
- A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine.Yearbook of medical informatics · 2017Review
- Enhancing Diversity in Biomedical Data Science.Ethnicity & disease · 2017Review
- THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.