ArticleCBE life sciences education2017
Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory.
Article in CBE life sciences education, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 7 citations in OpenAlex.
- Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.Briefings in bioinformatics · 2026Review
- Prioritization of the concepts and skills in quantitative education for graduate students in biomedical science.PloS one · 2023Article
- Data science in cell imaging.Journal of cell science · 2021Review
- Value of Collaboration among Multi-Domain Experts in Analysis of High-Throughput Genomics Data.Cancer research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program's effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational-experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas.
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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.