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RESEARCH PRODUCT
Prediction of Disease–lncRNA Associations via Machine Learning and Big Data Approaches
Armando La PlacaSimona E. RomboMariella Bonomosubject
business.industryComputer scienceBig Data Technologies Biological Processes Computational Approaches Disease–lncRNA Associations Non-Coding RNA Hypergeometric distribution Leave One Out Cross Validation Long non-coding RNA Master-Slave Architecture Micro-RNA.Big dataArtificial intelligenceDiseasebusinessMachine learningcomputer.software_genrecomputerdescription
This chapter introduces long non-coding RNAs and their role in the occurrence and progress of diseases. The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at the 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 the time and cost of biological experiments. We first review the main computational approaches proposed in the literature for the prediction of lncRNA-disease associations. Then we focus on one of these approaches that is based on network neighborhood analysis and big data technologies. For each of the described approaches, their performance on real datasets is reported in terms of area under the curve of the receiver operating characteristic.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-10-13 |