Prediction of lncRNA-disease associations from tripartite graphs


The discovery of nove llncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at lncRNA level, as well as to the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of potential disease-lncRNA associations can effectively decrease time and cost of biological experiments. We propose an approach for the prediction of lncRNA-disease associations based on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. The main idea here is to discover hidden relationships between lncRNAs and diseases through the exploration of their interactions with intermediate molecules (e.g., miRNAs) in the tripartite graph, based on the consideration that while a few of lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. The effectiveness of our approach is proved by its ability in the identification of associations missed by competitors, on real datasets.

Proceedings of the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, Sep 4, 2020, in conjunction with the 46th International Conference on Very Large Data Bases
Mariella Bonomo
PhD student

Phd Student

Armando La Placa
PhD student

PhD Student

Simona E. Rombo
Associate professor

Researcher on Bioinformatics, Network Analysis, Big Data