ReviewBioMed research international2013
Cancer vaccines: state of the art of the computational modeling approaches.
Review in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development.Current pharmaceutical design · 2021Review
- Mathematical Modelling and Analysis of the Tumor Treatment Regimens with Pulsed Immunotherapy and Chemotherapy.Computational and mathematical methods in medicine · 2016Article
- Mathematical Model of Innate and Adaptive Immunity of Sepsis: A Modeling and Simulation Study of Infectious Disease.BioMed research international · 2015Article
- In silico modeling of the immune system: cellular and molecular scale approaches.BioMed research international · 2014Review
- Agent-based modeling of the immune system: NetLogo, a promising framework.BioMed research international · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future.
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.