ArticleCancer informatics2016
A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.
Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Recurrent copy number alterations in young women with breast cancer.Oncotarget · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique. The algorithm has an optimal solution, which means all maximal cliques can be identified, and guarantees that the identified CNV regions are the most frequent and that the minimal regions have been delineated among tumor samples. The algorithm has successfully been applied to analyze a large cohort of breast cancer samples and identified some breast cancer-associated genes and pathways.
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.