Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.Keywords: lncRNA-protein interaction, computational method, network-based method, machine learning-based method, data repositories

Section: Other Methodsmentioning

confidence: 99%

“…The details are shown in Figure 12. Shen et al (2018) developed an LPI prediction algorithm, LPI-FKLKRR, combining a kernel ridge regression model based on fast kernel learning. LPI-FKLKRR can be broken into six steps:…”

Section: Other Methodsmentioning

confidence: 99%

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

Peng

Liu

Yang³

et al. 2020

“…A label propagation algorithm is another common recommendation algorithm, two models were built based on label propagation algorithms (Zhang et al, 2018a ; Zhu et al, 2019 ). Meanwhile, some other machine learning algorithms were also adapted in the prediction of lncRNA-protein interactions, including feature projection ensemble learning (Zhang et al, 2018b ), KATZ scoring schemes (Zhang et al, 2019 ), the kernel ridge regression algorithm (Shen et al, 2019 ), and the depth-first search algorithm (Zhang H. et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

Zhou

Shen

et al. 2020

Long non-coding RNAs (lncRNAs) play an important role in serval biological activities, including transcription, splicing, translation, and some other cellular regulation processes. lncRNAs perform their biological functions by interacting with various proteins. The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we proposed a novel model to predict potential lncRNA-protein interactions using the SKF (similarity kernel fusion) and LapRLS (Laplacian regularized least squares) algorithms. We named this method the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated into the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (area under receiver operating curve) of 0.909 in a 5-fold cross-validation, which outperforms other state-of-the-art methods. A total of 19 out of the top 20 ranked interaction predictions were verified by existing data, which implied that the LPI-SKF had great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository (https://github.com/zyk2118216069/LPI-SKF).

“…However, such experimental methods are laborious, time-consuming, and costly (Huang et al, 2012). Recently, various computational methods have been proposed to address the challenges in bioinformatics (He et al, 2018a,b; Zou et al, 2018), such as lncRNA-protein (Hu et al, 2017; Shen et al, 2019a,b), miRNA-disease (Chen and Huang, 2017; Chen et al, 2018a,b,d,e,f; Jiang et al, 2018a,b; Xie et al, 2018), drug-target (Chen et al, 2016b; Wang et al, 2017; Wu et al, 2018) and microbe-disease associations predictions (Chen et al, 2017a; Peng et al, 2018). The methods for inferring lncRNA-protein associations can roughly be classified into two types: the machine learning methods and the network-based methods.…”

Section: Introductionmentioning

confidence: 99%

LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

Xie

Sun

et al. 2019

According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.