ArticleInternational journal of molecular sciences2018
A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Targeting YAP: mechanistic breakthroughs and therapeutic prospects in reversing organ fibrosis.Molecular and cellular biochemistry · 2026Review
- Review
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Major-effect quantitative trait locus qLKR4.1 encodes a phospholipase Dδ protein associated with low-KTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Weight loss outcomes are not compromised in bariatric patients using cannabis.Surgical endoscopy · 2023Article
- The current landscape of microRNAs (miRNAs) in bacterial pneumonia: opportunities and challenges.Cellular & molecular biology letters · 2022Review
- TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network.Molecules (Basel, Switzerland) · 2022Article
- Review
- Computational Models in Non-Coding RNA and Human Disease.International journal of molecular sciences · 2020Article
- MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association.Frontiers in genetics · 2020Article
- A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations.Genes · 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
Abstract
Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA⁻disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA⁻disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA⁻disease associations (WINMDA) by integrating the
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.