ArticlePLoS computational biology2018
MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 169 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
169 citing papers in PubMed, 356 citations in OpenAlex.
- Integrating lncRNA data for prediction of miRNA-disease association using network fusion and matrix completion.Journal of computer-aided molecular design · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- miCDER: a context-aware transformer model for joint miRNA-disease entity and multi-level regulatory relation extraction.BMC genomics · 2025Article
- Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.Scientific reports · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction.Biology methods & protocols · 2025Article
- Predicting human miRNA disease association with minimize matrix nuclear norm.Scientific reports · 2024Article
- Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.BMC genomics · 2024Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer.Clinical and experimental medicine · 2024Article
- Molecular characterization and biomarker identification in paediatric B-cell acute lymphoblastic leukaemia.Journal of cellular and molecular medicine · 2024Article
- SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations.BMC genomics · 2024Article
- Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- EMCMDA: predicting miRNA-disease associations via efficient matrix completion.Scientific reports · 2024Article
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction.Briefings in bioinformatics · 2024Article
- HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.Non-coding RNA · 2024Article
- Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.PeerJ. Computer science · 2024Article
109 more citing papers are in PubMed but not listed here.
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature.
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.