ArticleBMC bioinformatics2020
Graph regularized L
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.Frontiers in microbiology · 2024Article
- Predicting miRNA-disease associations based on multi-view information fusion.Frontiers in genetics · 2022Article
- SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.PLoS computational biology · 2021Article
- ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.Frontiers in genetics · 2021Article
- WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting.Frontiers in genetics · 2021Article
- SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction.Frontiers in cell and developmental biology · 2021Article
- Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.BioMed research international · 2021Article
- RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network.BMC bioinformatics · 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
5 authors.
Funding
Abstract
backgroundThe aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.
resultsHere, we present a computational framework based on graph Laplacian regularized L
conclusionsThe new method (GRL
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.