ArticleiScience2020
Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- Disentangling direct and pleiotropic SNP effects in alfalfa (Medicago sativa L.) using causal graph learning.Scientific reports · 2026Article
- Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.Scientific reports · 2025Article
- Deformable and Fragile Object Manipulation: A Review and Prospects.Sensors (Basel, Switzerland) · 2025Review
- A potential therapeutic molecule target: lncRNA AK023507 inhibits the metastasis of breast cancer by regulating the WNT/DOCK4/β-catenin axis.Breast cancer research and treatment · 2025Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets.Sensors (Basel, Switzerland) · 2024Review
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.iScience · 2021Article
- In silico drug repositioning using deep learning and comprehensive similarity measures.BMC bioinformatics · 2021Article
- MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a random forest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components.
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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.