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ncPred: ncRNA-Disease Association Prediction through Tripartite Network-Based Inference
Salvatore Alaimo1, Rosalba Giugno2, Alfredo Pulvirenti2
1Department of Mathematics and Computer Science, University of Catania , Catania , Italy.
Motivation:
Over the past few years, experimental evidence has highlighted the role of microRNAs to human diseases. miRNAs are critical for the regulation of cellular processes, and, therefore, their aberration can be among the triggering causes of pathological phenomena. They are just one member of the large class of non-coding RNAs, which include transcribed ultra-conserved regions (T-UCRs), small nucleolar RNAs (snoRNAs), PIWI-interacting RNAs (piRNAs), large intergenic non-coding RNAs (lincRNAs) and, the heterogeneous group of long non-coding RNAs (lncRNAs). Their associations with diseases are few in number, and their reliability is questionable. In literature, there is only one recent method proposed by Yang et al. (2014) to predict lncRNA-disease associations. This technique, however, lacks in prediction quality. All these elements entail the need to investigate new bioinformatics tools for the prediction of high quality ncRNA-disease associations. Here, we propose a method called ncPred for the inference of novel ncRNA-disease association based on recommendation technique. We represent our knowledge through a tripartite network, whose nodes are ncRNAs, targets, or diseases. Interactions in such a network associate each ncRNA with a disease through its targets. Our algorithm, starting from such a network, computes weights between each ncRNA-disease pair using a multi-level resource transfer technique that at each step takes into account the resource transferred in the previous one.
Results:
The results of our experimental analysis show that our approach is able to predict more biologically significant associations with respect to those obtained by Yang et al. (2014), yielding an improvement in terms of the average area under the ROC curve (AUC). These results prove the ability of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes involved in complex diseases.
Availability:
All the ncPred predictions together with the datasets used for the analysis are available at the following url: http://alpha.dmi.unict.it/ncPred/
Insights
We developed ncPred, a new bioinformatics tool to predict non-coding RNA (ncRNA) and disease associations. ncPred improves upon existing methods by offering higher quality predictions for better understanding disease mechanisms.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- MicroRNAs (miRNAs) and other non-coding RNAs (ncRNAs) play crucial roles in cellular processes and human diseases.
- Current methods for predicting ncRNA-disease associations, like the one by Yang et al. (2014), have limitations in prediction quality.
- There is a need for advanced bioinformatics tools to accurately identify ncRNA-disease associations.
Purpose of the Study:
- To propose ncPred, a novel recommendation-based method for inferring ncRNA-disease associations.
- To improve the accuracy and reliability of predicting biologically significant ncRNA-disease links.
- To provide a valuable tool for understanding the molecular basis of complex diseases.
Main Methods:
- Developed ncPred, a bioinformatics tool utilizing a recommendation technique.
- Represented biological knowledge using a tripartite network (ncRNAs, targets, diseases).
- Employed a multi-level resource transfer technique to compute ncRNA-disease association weights.
Main Results:
- ncPred demonstrates superior performance in predicting biologically significant ncRNA-disease associations compared to existing methods.
- The approach achieved improved prediction accuracy, as indicated by the area under the ROC curve (AUC).
- The findings highlight ncPred's potential to advance the understanding of disease-related molecular processes.
Conclusions:
- ncPred is an effective bioinformatics tool for predicting high-quality ncRNA-disease associations.
- The method offers a significant improvement over previous prediction techniques.
- The tool and associated data are publicly available for further research.
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