Sun Xibo, Cheng Leiming, Liu Jinyang, Xie Cuinan, Yang Jiasheng, Li Fu
Yidu Central Hospital of Weifang, Weifang, China.
Huaibei Kuanggong Zong Yiyuan, Huaibei, China.
Front Genet. 2021 Jul 16;12:690096. doi: 10.3389/fgene.2021.690096. eCollection 2021.
Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental validation of interactions between lncRNAs and proteins is costly and time-consuming. In this study, we developed a weighted graph-regularized matrix factorization (LPI-WGRMF) method to find unobserved lncRNA-protein interactions (LPIs) based on lncRNA similarity matrix, protein similarity matrix, and known LPIs. We compared our proposed LPI-WGRMF method with five classical LPI prediction methods, that is, LPBNI, LPI-IBNRA, LPIHN, RWR, and collaborative filtering (CF). The results demonstrate that the LPI-WGRMF method can produce high-accuracy performance, obtaining an AUC score of 0.9012 and AUPR of 0.7324. The case study showed that SFPQ, SNHG3, and PRPF31 may associate with Q9NUL5, Q9NUL5, and Q9UKV8 with the highest linking probabilities and need to further experimental validation.
长链非编码RNA（lncRNAs）因其与许多关键生物活性密切相关而受到广泛关注。尽管大多数lncRNAs的确切功能尚不清楚，但研究表明lncRNAs通常通过与相应蛋白质相互作用来发挥生物学功能。lncRNAs与蛋白质之间相互作用的实验验证既昂贵又耗时。在本研究中，我们开发了一种加权图正则化矩阵分解（LPI-WGRMF）方法，以基于lncRNA相似性矩阵、蛋白质相似性矩阵和已知的lncRNA-蛋白质相互作用（LPI）来发现未观察到的LPI。我们将提出的LPI-WGRMF方法与五种经典的LPI预测方法进行了比较，即LPBNI、LPI-IBNRA、LPIHN、RWR和协同过滤（CF）。结果表明，LPI-WGRMF方法可以产生高精度的性能，获得的AUC评分为0.9012，AUPR为0.7324。案例研究表明，SFPQ、SNHG3和PRPF31可能与Q9NUL5、Q9NUL5和Q9UKV8以最高的连接概率相关联，需要进一步的实验验证。
