ArticleCancer informatics2019
Multiple Omics Data Integration to Identify Long Noncoding RNA Responsible for Breast Cancer-Related Mortality.
Article in Cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 3 citations in OpenAlex.
- Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data.Journal of oncology · 2022Article
- Prediction and prognostic significance of ALOX12B and PACSIN1 expression in gastric cancer by genome-wide RNA expression and methylation analysis.Journal of gastrointestinal oncology · 2021Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
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 at 2 institutions in 1 country.
Funding
Abstract
Long non-coding RNAs (lncRNAs) are a large and diverse class of transcribed RNAs, which have been shown to play a significant role in developing cancer. In this study, we apply integrative modeling framework to integrate the DNA copy number variation (CNV), lncRNA expression, and downstream target protein expression to predict patient survival in breast cancer. We develop a 3-stage model combining a mechanical model (lncRNA regressed on CNV and target proteins regressed on lncRNA) and a clinical model (survival regressed on estimated effects from the mechanical models). Using lncRNAs (such as
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.