ArticleFrontiers in physiology2012
Mathematical and statistical modeling in cancer systems biology.
Article in Frontiers in physiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma.PLoS computational biology · 2019Article
- Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis.BMC molecular and cell biology · 2019Article
- Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease.BMC bioinformatics · 2019Article
- Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach.Journal of translational medicine · 2018Article
- MicroRNA pharmacogenomics based integrated model of miR-17-92 cluster in sorafenib resistant HCC cells reveals a strategy to forestall drug resistance.Scientific reports · 2017Article
- Topological and kinetic determinants of the modal matrices of dynamic models of metabolism.PloS one · 2017Article
- Contribution of bioinformatics prediction in microRNA-based cancer therapeutics.Advanced drug delivery reviews · 2015Review
- A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics.Systems and synthetic biology · 2014Article
- Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles.Current pharmaceutical design · 2014Review
- Advances in systems immunology and cancer.Frontiers in physiology · 2014Article
- The role of engineering approaches in analysing cancer invasion and metastasis.Nature reviews. Cancer · 2013Review
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 is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.
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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.