LPI-KTASLP: Prediction of LncRNA-Protein Interaction by Semi-Supervised Link Learning With Multivariate Information

• Published in: IEEE access Vol. 7; pp. 13486 - 13496
• Main Authors: Shen, Cong, Ding, Yijie, Tang, Jijun, Jiang, Limin, Guo, Fei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Alignment; Artificial intelligence; Bandwidth; Gene expression; Kernel; kernel target alignment; Kernels; LncRNA-protein interaction; low-rank approximation; multiple kernel learning; Multivariate analysis; Nucleotides; Polynucleotides; Prediction models; Proteins; Ribonucleic acid; RNA; semi-supervised link prediction; Semisupervised learning; Supervised learning; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2894225

Long non-coding RNA, also known as lncRNA, is a series of single-stranded polynucleotides (no less than 200 nucleotides each), consisting of non-protein coding transcripts. LncRNA plays a crucial role in regulating gene expression, during the transcriptional, post-transcriptional, and epigenetic processes. This is achieved by lncRNA interacts with the corresponding RNA-binding proteins. It has been drawn to a lot of attention that the reduction of the excessive laboratory cost and the increase in speed and accuracy gains benefits from the employment of computational intelligence in lncRNA-protein interaction (LPI) identification. Although numerous pertinent in silico studies of LPI prediction have been proposed, there is still room for enhancing the accuracy of the existing LPI prediction methods. In this paper, we have proposed a novel method for identifying LPI with kernel target alignment based on semi-supervised link prediction (LPI-KTASLP), which adopts multivariate information to predict lncRNAs-proteins interactions. To integrate the heterogeneous kernels, kernel target alignment has been applied to deal with kernel fusion. We have calculated the low-rank approximation matrices of lncRNA and protein, where eigendecomposition is used to reduce computing pressure. The prediction model has been obtained by producing the ultimate LPI prediction matrix. Experimental results show that the prediction ability of the LPI-KTASLP algorithm has surpassed many other LPI prediction schemes. Our method of lncRNA-protein interaction prediction has been evaluated on a standard benchmark dataset of LPIs. We have observed that the highest AUPR of 0.6148 is obtained by our proposed model (LPI-KTASLP). This is superior to the integrated LPLNP (AUPR: 0.4584), the RWR (AUPR: 0.2827), the CF (AUPR: 0.2357), the LPIHN (AUPR: 0.2299), and the LPBNI (AUPR: 0.3302). It is very encouraging that most of the LPI predictions have been confirmed to be close to relevant concentrations.