ArticlePLoS computational biology2018
Eleven quick tips for architecting biomedical informatics workflows with cloud computing.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
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- Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024.bioRxiv : the preprint server for biology · 2025Article
- Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment.PLoS computational biology · 2023Article
- Accelerating genomic workflows using NVIDIA Parabricks.BMC bioinformatics · 2023Article
- Trellis for efficient data and task management in the VA Million Veteran Program.Scientific reports · 2021Article
- The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Twelve quick steps for genome assembly and annotation in the classroom.PLoS computational biology · 2020Article
- A cloud-based bioinformatic analytic infrastructure and Data Management Core for the Expanded Program on Immunization Consortium.Journal of clinical and translational science · 2020Article
- Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.Personalized medicine · 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.
Funding
Abstract
Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for architecting biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world's largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction.
Indexed as
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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.