ArticleBioMed research international2015
An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis.International journal of molecular sciences · 2023Pooled it
- An efficient pattern growth approach for mining fault tolerant frequent itemsets.Expert systems with applications · 2020Article
- Machine learning approaches to study glioblastoma: A review of the last decade of applications.Cancer reports (Hoboken, N.J.) · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain tumor datasets. The expression profiles of the ten lncRNAs appeared to be sufficient to distinguish between cancer and normal tissues. A further characterization of this lncRNAs signature through a comodulation expression analysis suggested that biological processes specific of the nervous system could be compromised.
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.