ArticleScientific reports2014
Randomness and preserved patterns in cancer network.
Article in Scientific reports, 2014. 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, 28 citations in OpenAlex.
- Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy.NPJ systems biology and applications · 2021Article
- Analysis of human mitochondrial genome co-occurrence networks of Asian population at varying altitudes.Scientific reports · 2021Article
- The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.Frontiers in aging neuroscience · 2020Article
- Random Matrix Analysis for Gene Interaction Networks in Cancer Cells.Scientific reports · 2018Article
- Dysregulated genes targeted by microRNAs and metabolic pathways in bladder cancer revealed by bioinformatics methods.Oncology letters · 2018Article
- Network spectra for drug-target identification in complex diseases: new guns against old foes.Applied network science · 2018Review
- Understanding cancer complexome using networks, spectral graph theory and multilayer framework.Scientific reports · 2017Article
- Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks.PloS one · 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
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.
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.