ArticleScientific reports2019
Fusion of multiple heterogeneous networks for predicting circRNA-disease associations.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Circular RNA profiling facilitates the diagnosis and prognostic monitoring of breast cancer: A pair-wise meta-analysis.Journal of clinical laboratory analysis · 2021Pooled it
- A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations.Scientific reports · 2026Article
- Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs.ACS omega · 2025Article
- Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.Biomolecules · 2025Article
- Identification of circRNA-disease associations via multi-model fusion and ensemble learning.Journal of cellular and molecular medicine · 2024Review
- BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.Frontiers in genetics · 2024Article
- Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion.Scientific reports · 2023Article
- MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.BMC bioinformatics · 2022Article
- Promising Roles of Circular RNAs as Biomarkers and Targets for Potential Diagnosis and Therapy of Tuberculosis.Biomolecules · 2022Review
- CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning.BMC bioinformatics · 2022Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- Identification and comparison of novel circular RNAs with associated co-expression and competing endogenous RNA networks in postmenopausal osteoporosis.Journal of orthopaedic surgery and research · 2021Article
- Overexpression of hsa_circ_0094742 inhibits IL-1β-induced decline in CHON-001 cell viability by targeting microRNA-127-5p.Histology and histopathology · 2021Article
- Potential Clinical Applications of Exosomal Circular RNAs: More than Diagnosis.Frontiers in molecular biosciences · 2021Review
- Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer.Frontiers in cell and developmental biology · 2021Review
- Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association.Scientific reports · 2020Article
- Prioritizing CircRNA-Disease Associations With Convolutional Neural Network Based on Multiple Similarity Feature Fusion.Frontiers in genetics · 2020Article
- MultiSourcDSim: an integrated approach for exploring disease similarity.BMC medical informatics and decision making · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circular RNAs (circRNAs) are a newly identified type of non-coding RNA (ncRNA) that plays crucial roles in many cellular processes and human diseases, and are potential disease biomarkers and therapeutic targets in human diseases. However, experimentally verified circRNA-disease associations are very rare. Hence, developing an accurate and efficient method to predict the association between circRNA and disease may be beneficial to disease prevention, diagnosis, and treatment. Here, we propose a computational method named KATZCPDA, which is based on the KATZ method and the integrations among circRNAs, proteins, and diseases to predict circRNA-disease associations. KATZCPDA not only verifies existing circRNA-disease associations but also predicts unknown associations. As demonstrated by leave-one-out and 10-fold cross-validation, KATZCPDA achieves AUC values of 0.959 and 0.958, respectively. The performance of KATZCPDA was substantially higher than those of previously developed network-based methods. To further demonstrate the effectiveness of KATZCPDA, we apply KATZCPDA to predict the associated circRNAs of Colorectal cancer, glioma, breast cancer, and Tuberculosis. The results illustrated that the predicted circRNA-disease associations could rank the top 10 of the experimentally verified associations.
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.