ArticleOncology letters2017
Identification of differentially expressed molecular functions associated with breast cancer using Gibbs sampling.
Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Construction of a Signature Model to Predict the Radioactive Iodine Response of Papillary Thyroid Cancer.Frontiers in endocrinology · 2022Article
- Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data.Frontiers in genetics · 2022Article
- Ribosome Proteins Represented by RPL27A Mark the Development and Metastasis of Triple-Negative Breast Cancer in Mouse and Human.Frontiers in cell and developmental biology · 2021Article
- Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data.Entropy (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify differentially expressed molecular functions (DEMFs) for breast cancer using the Gibbs sampling approach. Molecular functions (MFs) were obtained on the basis of the Bayesian Approach for Geneset Selection package. Subsequently, MFs were converted into Markov chains (MCs) prior to calculating their probabilities, utilizing the MC Monte Carlo algorithm. DEMFs were identified with probabilities ≥0.8 and the gene compositions were studied. Finally, a co-expression network was constructed via the empirical Bayes method and a pathway enrichment analysis of genes in DEMFs was performed. A total of 396 MFs were identified and all transformed to MCs. With the threshold, 2 DEMFs (structural molecule activity and protein heterodimerization activity) were obtained. The DEMFs were comprised of 297 genes, 259 of which were mapped to the co-expression network. These 297 genes were identified to be enriched in 10 pathways, and ribosome was the most significant pathway. The results of the present study revealed 2 DEMFs (structural molecule activity and protein heterodimerization activity) which may be associated with the pathological molecular mechanisms underlying breast cancer, based on Gibbs sampling.
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.