ArticleFrontiers in genetics2022
miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.
Article in Frontiers in genetics, 2022. 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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Who cites it
12 citing papers in PubMed.
- Unbiased microRNA-Disease Association Prediction Using ICD-11 Codes and Negative Sampling.Pharmacology research & perspectives · 2025Article
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.PeerJ. Computer science · 2025Article
- Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification.Computational and structural biotechnology journal · 2025Article
- Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach.Scientific reports · 2024Article
- Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration.ACS pharmacology & translational science · 2024Review
- Article
- Invention of 3Mint for feature grouping and scoring in multi-omics.Frontiers in genetics · 2023Article
- microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach.Frontiers in microbiology · 2023Article
- TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information.Frontiers in genetics · 2023Article
- GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning.Frontiers in genetics · 2023Article
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4 authors.
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Abstract
During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated
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