ReviewWiley interdisciplinary reviews. Systems biology and medicine
Accelerating cancer systems biology research through Semantic Web technology.
Review in Wiley interdisciplinary reviews. Systems biology and medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Multiscale computational modeling of cancer growth using features derived from microCT images.Scientific reports · 2021Article
- Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System.Cancer informatics · 2018Review
- Integrated PK-PD and agent-based modeling in oncology.Journal of pharmacokinetics and pharmacodynamics · 2015Review
- An evidence-based approach to identify aging-related genes in Caenorhabditis elegans.BMC bioinformatics · 2015Article
- Simulating cancer growth with multiscale agent-based modeling.Seminars in cancer biology · 2015Review
- Article
- Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.Wiley interdisciplinary reviews. Systems biology and medicineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers' intellectual property.
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.