Function prediction of cancer-related LncRNAs using heterogeneous information network model. (30th March 2019)

• Record Type: Journal Article
• Authors: Kumar, P.V. Sunil; Manju, M.; Gopakumar, G.
• Is Part Of: International journal of data mining and bioinformatics. Volume 21:Number 4(2018)
• Page Start: 315
• Page End: 338
• Publication Date: 2019-03-30
• Subjects: LncRNA -- cancer -- heterogeneous information network -- meta-path -- classification -- support vector machine -- machine learning -- oncogenic -- tumour suppressor; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

The aberrant expression of lncRNAs is proven to be one of the prime reasons for cancer progression. Recent studies recommend lncRNAs as potential therapeutic target in cancer. The overexpression of oncogenic lncRNAs causes tumour progression, whereas that of tumour suppressor lncRNAs leads to apoptosis. In this paper, a heterogeneous information network-based Support Vector Machine classifier that can predict lncRNAs into oncogenic or tumour suppressor is proposed. Interactions of lncRNAs with other lncRNAs and proteins along with protein-protein interactions are used to build the network. The model predicted lncRNAs into oncogenic or tumour suppressor with an accuracy of 0.83 and produced an accuracy of 0.80 during an independent validation. A comparison with recently reported studies shows that prediction results fall in line with them.