ReviewF1000Research2017
General guidelines for biomedical software development.
Review in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Should we abandon the patient-specific instrumentation ship in total knee arthroplasty? Not quite yet!BMC musculoskeletal disorders · 2021Pooled it
- BioTester: an AI-driven automated testing framework for identifying potential quality risks in bioinformatics software.Briefings in bioinformatics · 2026Article
- Steering veridical large language model analyses by correcting and enriching generated database queries: first steps toward ChatGPT bioinformatics.Briefings in bioinformatics · 2024Article
- Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.Journal of cheminformatics · 2024Article
- Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).European journal of medical research · 2024Article
- Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.Genome biology · 2024Article
- NFTest: automated testing of Nextflow pipelines.Bioinformatics (Oxford, England) · 2024Article
- A choice, not an obligation : Releasing scientific software as open source should be the responsibility of the authors.EMBO reports · 2024Article
- PipeVal: light-weight extensible tool for file validation.Bioinformatics (Oxford, England) · 2024Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool.Scientific data · 2023Article
- Foundations of plasma standards.Plasma sources science & technology · 2023Article
- Validating the knowledge bank approach for personalized prediction of survival in acute myeloid leukemia: a reproducibility study.Human genetics · 2022Article
- Article
- Infobuttons for Genomic Medicine: Requirements and Barriers.Applied clinical informatics · 2021Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Using bio.tools to generate and annotate workbench tool descriptions.F1000Research · 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
No grant is acknowledged in the PubMed record.
Abstract
Most bioinformatics tools available today were not written by professional software developers, but by people that wanted to solve their own problems, using computational solutions and spending the minimum time and effort possible, since these were just the means to an end. Consequently, a vast number of software applications are currently available, hindering the task of identifying the utility and quality of each. At the same time, this situation has hindered regular adoption of these tools in clinical practice. Typically, they are not sufficiently developed to be used by most clinical researchers and practitioners. To address these issues, it is necessary to re-think how biomedical applications are built and adopt new strategies that ensure quality, efficiency, robustness, correctness and reusability of software components. We also need to engage end-users during the development process to ensure that applications fit their needs. In this review, we present a set of guidelines to support biomedical software development, with an explanation of how they can be implemented and what kind of open-source tools can be used for each specific topic.
Indexed as
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.