ArticleScience progress
The roles of code in biology.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Nrf1 acts as a highly-conserved determinon for maintaining robust redox homeostasis in the eco-evo-devo process of life histories.Cell stress · 2025Review
- Improving bioinformatics software quality through incorporation of software engineering practices.PeerJ. Computer science · 2022Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a "sweet spot" in the taxonomy-a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the "inverse Conway maneuver" to shape a research team so as to allow dedicated software engineers to interface with researchers working in this "sweet spot." We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.
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Identifiers
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.