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SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

  • Wen Zhang, 
  • Xiang Yue, 
  • Guifeng Tang, 
  • Wenjian Wu, 
  • Feng Huang, 
  • Xining Zhang
PLOS
x

Abstract

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

Author summary

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. In this paper, we propose a novel computational method “SFPEL-LPI” to predict lncRNA-protein interactions. SFPEL-LPI makes use of lncRNA sequences, protein sequences and known lncRNA-protein associations to extract features and calculate similarities for lncRNAs and proteins, and then combines them with a feature projection ensemble learning frame. SFPEL-LPI can predict unobserved interactions between lncRNAs and proteins, and also can make predictions for new lncRNAs (or proteins), which have no interactions with any proteins (or lncRNAs). SFPEL-LPI produces high-accuracy performances on the benchmark dataset when evaluated by five-fold cross validation, and outperforms state-of-the-art methods. The case studies demonstrate that SFPEL-LPI can find out novel associations, which are confirmed by literature. To facilitate the lncRNA-protein interaction prediction, we develop a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

This is a PLOS Computational Biology Methods paper.

Introduction

Long noncoding RNAs (lncRNAs) are a class of transcribed RNA molecules with a length of more than 200 nucleotides that do not encode proteins [1,2]. Since lncRNAs are involved in important biological regulations [35], lncRNAs have gained widespread attention. Studies [59] revealed that lncRNAs can interact with proteins, and then activate post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNAs’ functions. There exist a large number of unexplored lncRNAs and proteins, which makes it impossible to examine their interactions efficiently and effectively through wet experiments.

In recent years, many computational methods have been proposed to predict lncRNA-protein interactions, in order to screen lncRNA-protein interactions and guide wet experiments. There are two types of computational methods: binary classification methods and semi-supervised learning methods. The binary classification methods take known interacting lncRNA-protein pairs as positive instances and non-interacting pairs as negative instances, and build binary classification-based models. Muppirala et al. [10] adopted the k-mer composition to encode RNA sequences and protein sequences, and used SVM and random forest to build prediction models. Wang et al. [11] used RNA-protein interactions as positive instances, and randomly selected twice number of protein-RNA pairs without interaction information as negative samples, and then built prediction models by using naive Bayes. Suresh et al. [12] proposed a support vector machine-based predictor “RPI-Pred” to predict protein-RNA interactions based on their sequences and structures. Xiao et al. [13] used the HeteSim measure to score lncRNA-protein pairs, and then built an SVM classifier based on HeteSim scores. However, binary classification-based methods are influenced by the imbalance ratio between positive instances and negative instances, and how to select high-quality negative instances is challenging. Semi-supervised learning methods formulate the lncRNA-protein interaction prediction as semi-supervised learning tasks. Lu et al. [14] used matrix multiplication to score each RNA-protein pair for prediction. Li et al. [15] proposed a heterogeneous network-based method “LPIHN”, which integrated the lncRNA-lncRNA similarity network, the lncRNA-protein interaction network and the protein-protein interaction network. Then, a random walk with restart was implemented on the heterogeneous network to infer lncRNA-protein interactions. Yang et al. [16] proposed the Hetesim algorithm, which can predict lncRNA-protein relation based on the heterogeneous lncRNA-protein network. Ge et al. [17] proposed a computational method “LPBNI” based on the lncRNA-protein bipartite network inference. Zheng et al. [18] constructed multiple protein-protein similarity networks to predict lncRNA-protein interactions. Zhang et al. [19] employed KATZ measure to calculate similarities between lncRNAs and proteins in a global network, which were constructed based on lncRNA-lncRNA similarity, lncRNA-protein associations and protein-protein interactions. Hu et al. [20] presented the eigenvalue transformation-based semi-supervised link prediction method “LPI-ETSLP”. Zhang et al. [21] proposed a linear neighborhood propagation method (LPLNP) by combining interaction profiles, expression profiles, sequence composition of lncRNAs and interaction profile, CTD feature of proteins. Moreover, there are related works about the DNA-protein binding prediction [22,23].

Existing computational methods utilize diverse lncRNA features and protein features, but features are not available for all lncRNAs or proteins, and these methods cannot work when information is unavailable. In addition, many lncRNAs (or proteins) don’t have known interactions with any protein (or lncRNA), and we name them as new lncRNAs (or proteins). Most existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins).

In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. Computational experiments demonstrate that SFPEL-LPI predicts lncRNA-protein associations accurately and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions.

Materials and methods

Dataset

Several databases facilitate the lncRNA-protein interaction prediction. NPInter database [24] includes experimental interactions among non-coding RNA and biomolecules (i.e. proteins, genomic DNAs and RNAs). NONCODE is an integrated information resource for non-coding RNAs. SUPERFAMILY [25] is a database of structural and functional annotation for all proteins and genomes. As far as we know, lncRNA-protein interactions from NPInter v2.0 database were widely used in related studies [20,21,2629]. Based on NPInter v2.0 interactions, we compiled a dataset containing 4158 lncRNA-protein interactions between 990 lncRNAs and 27 proteins. Moreover, we collected the sequences of these lncRNAs and proteins from NONCODE and SUPERFAMILY respectively. We adopt NPInter v2.0 dataset as the benchmark dataset to test the performances of prediction models.

Here, we introduce notations about the dataset. Given a set of lncRNAs and a set of proteins , known lncRNA-protein interactions can be represented by an s×t interaction matrix Y, where Yij = 1 if the lncRNA Li interacts with the protein Pj, otherwise Yij = 1.

Features for lncRNAs and proteins

In this section, we describe two lncRNA features and two protein features, based on lncRNA sequences, protein sequences and known lncRNA-protein interactions. On one hand, a great number of features [3036] can be extracted from lncRNAs sequences and proteins sequences, and feature-extraction tools such as Pse-in-One[37], BioSeq-Analysis[38], repRNA[39] [40], iMiRNA-PseDPC [41] and UltraPse [42] have been available. One the other hand, known lncRNA-protein interactions can bring features to describe lncRNAs and proteins.

LncRNA features.

The pseudo dinucleotide composition (PseDNC) [4346] describes the contiguous local sequence-order information and the global sequence-order information of lncRNAs. The pseudo dinucleotide composition has several variants, and we use the parallel correlation pseudo dinucleotide composition, which contains the occurrences of different dinucleotides and the physicochemical properties of dinucleotides. The PseDNC feature vector of an RNA sequence L is defined as: where where fk is the normalized occurrence frequency of dinucleotide in the RNA sequence L; the parameter τ is an integer, representing the highest counted rank of the correlation along an RNA sequence; w is the weight factor ranging from 0 to 1; θj is the j-tier correlation factor reflecting the sequence-order correlation between all the j-th most contiguous dinucleotides along an RNA sequence. We obtain PseDNC feature vectors of lncRNAs by using the python package "repDNA”, and more details about PseDNC are described in [40].

Moreover, we define the interaction profiles (IP) of lncRNAs based on known lncRNA-protein interactions. For a lncRNA Li, its interaction profile is a binary vector encoding the presence or absence of interactions with every protein, denoted as . Actually, the interaction profile of a lncRNA corresponds to a row vector of the interaction matrix Y, .

Protein features.

The pseudo amino acid composition (PseAAC) [4749] describes the amino acid composition and the sequence-order information of proteins, and has been widely used for tasks in bioinformatics. PseAAC contains 20 components reflecting the occurrence frequency of amino acids in a protein as well as the additional factors reflecting sequence-order information. Thus, we use PseAAC as a feature to represent proteins. There are several variants of PseAAC, and we adopt the parallel correlation pseudo amino acid composition. The PseAAC feature vector of a protein sequence P is defined as: where where fi is the normalized occurrence frequency of the 20 amino acids in the protein sequence P; the parameter τ is an integer, representing the highest counted rank of the correlation along a protein sequence; w is the weight factor ranging from 0 to 1; θj is the j-tier correlation factor reflecting the sequence-order correlation between all the j-th most contiguous residues along a protein sequence. We obtain the PseAAC feature vectors of proteins by using web server “Pse-in-One”, and more details are described in [37].

Similar to the lncRNA interaction profiles, the protein interaction profile (IP) of a protein Pi is a binary vector specifying the presence or absence of interactions with every lncRNAs, denoted as . The interaction profile of a protein corresponds to a column vector of the interaction matrix Y, .

Similarities for lncRNAs and proteins

In this section, we describe three lncRNA-lncRNA similarities and three protein-protein similarities.

LncRNA-lncRNA similarities.

As introduced in Section “LncRNA features”, we have two lncRNA features: PseDNC and IP, and thus use them to calculate two types of lncRNA-lncRNA similarities. There are different approaches to calculate similarity based on feature vectors, such as Jaccard similarity, Gauss similarity and cosine similarity. Here, we adopt the linear neighborhood similarity (LNS), which has been proposed in our previous work and successfully applied to many bioinformatics problems [21,34,50].

Moreover, we define the Smith Waterman subgraph similarity (SWSS) for lncRNAs. Smith Waterman algorithm [51] is a powerful tool to calculate similarity between biological sequences, but Smith Waterman algorithm only takes the sequence information into account. By considering sequence information and interactions information, we define Smith Waterman subgraph similarity (SWSS) between lncRNA Li and lncRNA Lj as, (15 where SW(Po1,Po2) is the Smith Waterman score between protein Po1 and protein Po2. A(Li) and A(Lj) are the set of proteins which interact with Li and Lj. n1 = |A(Li)| and n2 = |A(Lj)|.

Therefore, we obtain three lncRNA-lncRNA similarities: PseDNC similarity, IP similarity and SWSS similarity.

Protein-protein similarities.

As introduced in Section “Protein features”, we have two proteins features: PseAAC and IP. We also calculate two types of similarities by using the linear neighborhood similarity measure.

Similarly, we can calculate the Smith Waterman Subgraph Similarity (SWSS) between two proteins Pi and Pj, (2) where SW(Po1,Po2) is the Smith Waterman score between lncRNA Lo1 and lncRNA Lo2. A(Pi) and A(Pj) are the set of lncRNAs which interact with protein Pi and protein Pj. m1 = |A(Pi)| and m2 = |A(Pj)|.

Therefore, we obtain three protein-protein similarities: PseAAC similarity, IP similarity and SWSS similarity.

Feature projection ensemble learning method

Combining various features or fusing various features can usually lead to high-accuracy models [5258]. We have n features for lncRNAs (or proteins), denoted as n feature matrices , and have m types of similarities for lncRNAs (or proteins), denoted as m similarity matrices . The predicted lncRNA-protein interaction matrix is denoted as R. The known lncRNA-protein interaction matrix is denoted as Y. The flowchart of the feature projection ensemble learning method SFPEL-LPI is shown in Fig 1.

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Fig 1. The flowchart of SFPEL-LPI for predicting lncRNA-protein interactions.

https://doi.org/10.1371/journal.pcbi.1006616.g001

Objective function.

First, lncRNA (or protein) feature matrices are respectively projected to the predicted lncRNA-protein interaction matrix R by using the projection matrices . We estimate the projection matrices for features by minimizing the squared error between their products and the predicted lncRNA-protein interaction matrix R. So we have: (3) where is the Frobenius norm, and the projection matrices are required to be nonnegative.

Then, we introduce the -norm regularization term of to ensure the smoothness of the projection matrices. The predicted matrix R should be approximated to the known interaction matrix Y. We can have (4) where λ is the regularization coefficient, and μ is a trade-off parameter. .

Local structure of data can be maintained effectively through constructing a weighted graph or a similarity graph on a scatter of data points. For example, Xu et al. [59] introduced the manifold regularization term to preserve the visual feature manifold structure. Nie et al. [60], Bai et al. [61], Cai et al. [62,63] adopted graph Laplacian matrix to keep the graph’s local structure. Moreover, the Studies [34,6467] revealed that the combination of multiple similarities helps to improve performances. Inspired by pioneer work, we define a novel ensemble graph Laplacian regularization: (5) where Di is a diagonal matrix whose diagonal elements are corresponding row sums of Wi, and θ = [θ1,θ2,⋯,θi,⋯,θm] is a weight vector which is introduced to control the contribution of different graph Laplacian regularizations, and tr(∙) is the trace of a matrix. η>1 is the exponent of θ, which ensures that all graph Laplacian regularizations contribute effectively for the maintaining of graph local structures.

By combining (4) and (5), we obtain the objective function of SFPEL-LPI: (6) We introduce the Lagrangian function (Lf) to solve the optimization problem in (6), We calculate the partial derivatives of above function with respect to R, Gi and θi, and obtain the update rules about R, θi and Gi (proof and deduction are provided in S1 File): (7) (8) (9) where e is a column vector with all elements equal to 1, and has the same column dimensions as Xi. ⨀ denotes element-wise multiplication (also well known as Hadamard product), and the division in (9) is element-wise division. We separate the positive and negative parts of matrix A as (10)

Thus, we update R, Gi and θi based on (7), (8) and (9) alternatively until convergence.

Algorithms.

Following the method proposed in the Section “Objective function”, SFPEL-LPI can predict unobserved interactions between known lncRNAs and proteins. First, based on the lncRNA’s features, similarities and lncRNA-protein interactions, the prediction matrix Rl could be obtained. Similarly, using protein’s features, similarities and protein-lncRNA interactions, the prediction matrix Rp could be calculated. Then, SFPEL-LPI integrates the predictions based on lncRNAs and proteins as M = (Rl+(Rp)T)/2. Therefore, the unobserved interactions are scored in the corresponding entries of M. Algorithm 1 describes how SFPEL-LPI predicts unobserved associations between known lncRNAs and known proteins.

In addition, SFPEL-LPI could also be applied to predict proteins (or lncRNAs) interacting with new lncRNAs (or proteins). After using Algorithm 1 to train the model, the projection matrix and the weighting parameters of lncRNA’s features as well as protein’s features: Glu, Glv, θlu and θlv could be obtained. Then, we can use the features of new lncRNAs (or proteins) and the trained parameters to predict their predictions. Algorithm 2 describes how SFPEL-LPI finishes this task.

Algorithm 1: Predicting unobserved associations between known lncRNAs and known proteins by SFPEL-LPI.

Input: observed lncRNA-protein interaction matrix, Yl; observed protein-lncRNA interaction matrix, Yp = YlT; lncRNA feature matrices, {Xl1,Xl2,…,Xln}; protein feature matrices, {Xp1,Xp2,…,Xpn}; lncRNA normalized similarity matrices, {Wl1,Wl2,…,Wlm}; protein normalized similarity matrices, {Wp1,Wp2,…,Wpm}; regularization parameter, μ>0,λ>0; exponent parameter, η>1;

Output: lncRNA-protein interaction prediction matrix, M; predicted lncRNA-protein interaction matrix, Rl; predicted protein-lncRNA interaction matrix, Rp; projection matrices of lncRNA features {Gl1,Gl2,…,Gln}; projection matrices of protein features {Gp1,Gp2,…,Gpn}; weighting parameters of lncRNA similarity matrices, {θl1,θl2,…,θlm}; weighting parameters of protein similarity matrices, {θp1,θp2,…,θpm};

Initialize:

for each i(1≤in)

    initialize Gli, Gpi with random values on interval [0,1];

end for

for each i(1≤im)

    initialize θli, θpi as 1/m;

end for

repeat

    update Rl via (7) with fixing , ;

    for each i(1≤in)

        update Gli via (8) with fixing Rl;

    end for

    for each i(1≤im)

        update θli via (9) with fixing Rl:

    end for

until Converges;

repeat

    update Rp via (7) with fixing , ;

    for each i(1≤in)

        update Gpi via (8) with fixing Rp;

    end for

    for each i(1≤im)

        update θpi via (9) with fixing Rp:

    end for

until Converges;

M = (Rl+(Rp)T)/2

Return M

Algorithm 2: Predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins) by SFPEL-LPI

Input: feature matrices for new lncRNAs, {Xl1,Xl2,…,Xlu} (or feature matrices for new proteins, {Xp1,Xp2,…,Xpv}); projection matrices of lncRNA features {Gl1,Gl2,…,Glu} (or projection matrices of protein features {Gp1,Gp2,…,Gpv}); weighting parameters of lncRNA similarity matrices, {θ11,θl2,…,θlu} (or weighting parameters of protein features, {θp1,θp2,…,θpv}); (, or are obtained by Algorithm 1);

Output: predicted lncRNA-protein interaction matrix, (or predicted protein-lncRNA interaction matrix, );

Results

Evaluation metrics

We adopt five-fold cross validation to evaluate the performances of prediction models. The proposed method SFPEL-LPI can predict unobserved interactions between known lncRNAs and known proteins, and also can make predictions for new lncRNAs (or proteins). In predicting unobserved lncRNA-protein interactions, all known lncRNA-protein interactions are randomly split into five subsets with equal size. Each time, four subsets are combined as training set and the remaining one subset is used as the testing set. In predicting proteins interacting with new lncRNAs, all known lncRNAs are split into five subsets with equal size. The model is constructed based on the lncRNAs in training set and their interactions with all proteins, and then is used to predict proteins interacting with testing lncRNAs. Similarly, we evaluate the performances of models in predicting lncRNAs interacting with new proteins. Hence, we introduce notations for above mentioned cross validation settings. CVlp: known lncRNA-protein interactions are split into five folds in predicting unobserved interactions. CVl: known lncRNAs are split into five folds in predicting interactions for new lncRNAs. CVp: known proteins are split into five folds in predicting interactions for new proteins.

The area under ROC curve (AUC) and the area under precision-recall curve (AUPR) are popular metrics for evaluating prediction models. Since known lncRNA-protein interactions are much less than non-interacting lncRNA-protein pairs, we adopt AUPR as the primary metric, which punishes false positive more in the evaluation process[68,69]. Moreover, we adopt several binary classification metrics, i.e. recall (REC), accuracy (ACC), precision (PR) and F1-measure (F1).

Parameter setting

SFPEL-LPI has three parameters: μ, λ and η. μ is a parameter for the error between projected interactions and predicted lncRNA-protein interactions; λ controls the contribution of projection matrix; η describes strength of different similarity measures.

To test influence of parameters, we consider all combinations of parameters μ∈{10−4,10−3,10−2,10−1,100,101,102,103}, μ∈{10−4,10−3,10−2,10−1,100,101,102,103} and η∈{21,22,23,24,25,26,27,28}. We build SFPEL-LPI models by using different parameters, and implement five-fold cross validation CVlp to evaluate SFPEL-LPI models. SFPEL-LPI produces the best AUPR score of 0.473 when μ = 10−3, λ = 10−4 and η = 22. Then, we fix the parameter η = 22, and evaluate the influence of μ and λ. As shown in Fig 2A, μ greatly influences the performance of SFPEL-LPI, and a smaller value for μ is likely to produce better result. Further, we fix the parameters μ = 10−3 and λ = 10−4 and test the influence of η. As illustrated in Fig 2B, the performances of SFPEL-LPI decrease as η increases, and then remain unchanged after a threshold.

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Fig 2. The influence of parameters on AUPR of models.

(A) Fix the parameter η = 22, and evaluate the influence of parameters μ and λ. (B) Fix the parameter μ = 10−1, λ = 103, and evaluate the influence of parameter η.

https://doi.org/10.1371/journal.pcbi.1006616.g002

The parameter η is the index of similarity weights, and could control the relative contributions of different similarities. When fixing μ = 10−3 and λ = 10−4, we analyze the relation between η and lncRNA similarity measures θlncRNA (or protein similarity measures θprotein). As shown in Fig 3, similarities usually make different contributions to SFPEL-LPI models, and interaction profile similarities usually make more contributions than other similarities. With increase of η, different similarities are likely to make equal contributions.

Based on above discussion, we adopt μ = 10−3, λ = 10−4 and η= 22 for SFPEL-LPI in the following studies.

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Fig 3.

(A) The relationship between η and θlncRNA. (B) The relationship between η and θprotein.

https://doi.org/10.1371/journal.pcbi.1006616.g003

Performances of SFPEL-LPI

SFPEL-LPI can predict unobserved lncRNA-protein interactions between known lncRNAs and known proteins, and also can make predictions for new lncRNAs (or proteins). For different tasks, we adopt different evaluation schemes to split instances and implement five-fold cross validation under settings: CVlp, CVl and CVp.

Table 1 displays AUPR scores and AUC scores of SFPEL-LPI evaluated by CVlp, CVl and CVp. According to previous studies [7072], a prediction model that can accurately recover the true interacting proteins (or lncRNAs) is usually desired and useful for the wet experimental validation. Thus, we calculate the proportion of correctly predicted true interactions at different top-ranked percentiles under CVl or CVp. A new matric “recall @ top-ranked k %” is defined as the fraction of true interacting proteins (or lncRNAs) that are retrieved in the list of top-ranked k% predictions for a lncRNA (or protein). In Fig 4A, SFPEL-LPI performs effectively in predicting proteins (or lncRNAs) interacting with new lncRNAs (or proteins). The reason why the performances of predicting lncRNAs interacting with new proteins is not as well as the performances of predicting proteins interacting with new lncRNAs is that the number of lncRNAs (990) in our dataset is much more than the number of proteins (27). Consequently, less information is used to train SFPEL-LPI models.

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Fig 4.

(A) The average recalls in predicting new lncRNAs (or proteins) at different top-ranked percentiles under CVl or CVp. (B) The AUC value and AUPR value of predicting interacting lncRNAs for selected new proteins.

https://doi.org/10.1371/journal.pcbi.1006616.g004

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Table 1. Performances of SFPEL-LPI for predicting lncRNA-protein interactions.

https://doi.org/10.1371/journal.pcbi.1006616.t001

To further test capability of SFPEL-LPI for new proteins, we randomly select ten proteins to conduct experiments. In each experiment, a protein is used as the testing protein, and the model is constructed based on other proteins, all lncRNAs and their associations, and then predict lncRNAs interacting with the testing protein. AUC scores and AUPR scores are calculated based on the results for each protein. As shown in Fig 4B, SFPEL-LPI produces the AUPR values greater than 0.6 and the AUC values greater than 0.7 for most proteins, indicating great potential of predicting lncRNAs interacting with new proteins.

Comparison with state-of-the-art prediction methods

Several state-of-the-art computational methods have been proposed to predict lncRNA-protein interactions. Here, we adopt RWR[17], LPBNI[17], KATZLGO[19], LPI-ETSLP [20] and LPLNP [21] for comparison. RWR implemented random walk with restart to predict lncRNA-protein interactions. LPBNI constructed a lncRNA-protein bipartite network based on known lncRNA-protein interactions, and then predicted lncRNA-protein interactions by using the resource allocation algorithm. KATZLGO constructed a heterogeneous network based on lncRNA-lncRNA similarity, lncRNA-protein interactions and protein-protein similarity, and then adopted KATZ measure to calculate distances between lncRNAs and proteins in the network. LPI-ETSLP calculated lncRNA-lncRNA similarity and protein-protein similarity based on pairwise sequence Smith-Waterman scores, and then built semi-supervised link prediction classifier based on these similarities. LPNLP calculated three lncRNA-lncRNA similarities and two protein-protein similarities by using linear neighborhood similarity measure, and implemented label propagation to develop the integrated models.

First, we respectively build different prediction models based on the benchmark dataset. The benchmark methods were designed to predict unobserved interaction between know lncRNAs and know proteins. Therefore, we implement these methods and mainly evaluate their performances in predicting unobserved interactions under CVlp. As shown in Table 2, the AUPR values of RWR, LPBNI, KATZLGO, LPI-ETSLP, LPLNP and SFPEL-LPI are 0.236, 0.330, 0.286, 0.322, 0.459, 0.473, and AUC values are 0.850, 0.856, 0.760, 0.889, 0.910 and 0.920, respectively. SFPEL-LPI outperforms these five methods, and makes 100.4%, 43.3%, 65.4%, 46.9%, 3.1% improvements in terms of AUPR scores and 8.2%, 7.5%, 21.1%, 3.5%, 1.1% improvements in terms of AUC scores when compared with five benchmark methods. Though SFPEL-LPI produces slightly better performances than LPLNP in terms of AUPR and AUC, LPLNP utilizes more information than SFPEL-LPI for modeling. To be more specific, LPLNP uses three lncRNA features (“interaction profile”, “expression profile”, “sequence composition”) and two protein features (“interaction profile”, “CTD”), while SFPEL-LPI only used lncRNA sequences, protein lncRNAs and known lncRNA-protein interactions.

We conduct 20 runs of five-fold cross validation to evaluate methods, and take the paired t-test to analyze difference between SFPEL-LPI and benchmark methods. Table 3 demonstrates that SFPEL-LPI produces significantly better results than state-of-the-art methods in terms of AUC and AUPR.

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Table 3. Difference between SFPEL-LPI and benchmark methods tested by Paired t-test in terms of AUPR and AUC.

https://doi.org/10.1371/journal.pcbi.1006616.t003

The computational complexity is important for a computational method. To test the efficiency of SFPEL-LPI, we repeat 5-fold cross validation 20 times and compare running time of different methods on a PC with an Intel i7 7700k CPU and 16GB RAM. SFPEL-LPI costs the reasonable running time (29.42s) when compared with RWR (25.83s), LPBNI (4.01s), KATZLGO (4.36s), LPI-ETSLP (4.56s) and LPLNP (1337.64s).

Further, we randomly perturb all known lncRNA-protein interactions to test the robustness of prediction methods. To be more specific, we randomly remove 5% of known lncRNA-protein interactions and add the same number of inexistent interactions, and then compile the perturbed dataset. We build different prediction models based on the perturbed dataset and evaluate their performances. Clearly, data perturbation brings noise, and decreases the performances of prediction models. As displayed in Fig 5, AUC scores of RWR, LPBNI, KATZLGO, LPI-ETSLP, LPLNP, SFPEL-LPI are 0.812, 0.820, 0.735, 0.865, 0.874 and 0.889; AUPR scores are 0.192, 0.268, 0.225, 0.271, 0.343 and 0.351. Although prediction models produce lower performances than that in Table 2, SFPEL-LPI still produces satisfying results, and outperforms RWR, LPBNI, KATZLGO, LPI-ETSLP and LPLNP.

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Fig 5. Performance of different methods on the perturbed dataset.

(A) ROC curves. (B) PR curves.

https://doi.org/10.1371/journal.pcbi.1006616.g005

Independent experiments

Here, we conduct independent experiments to evaluate the practical ability of SFPEL-LPI. As described in Section “Dataset”, NPInter v2.0 dataset was compiled from the V2.0 edition of NPInter database. NPInter database has been updated to V3.0 edition, and contains newly discovered lncRNA-protein interactions. Therefore, we train the prediction model based on the NPInter v2.0 dataset and predict new lncRNA-protein interactions, and then check up on predictions in the NPInter database. Fig 6 shows the number of confirmed interactions in top 20 predictions of all methods. Clearly, SFPEL-LPI finds out more interactions than benchmark methods. In addition, we observe that most of novel interactions identified by SFPEL-LPI have low ranks in the predictions of other benchmark methods, indicating that SFPEL-LPI can find out interactions ignored by these methods. Top predictions and their ranks are provided in S1 Table.

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Fig 6. The number of confirmed lncRNA-protein interactions in top 20 predictions of different methods.

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Web server

We develop a web server based on SFPEL-LPI to facilitate the lncRNA-protein interaction prediction, available at http://www.bioinfotech.cn/SFPEL-LPI/. Users can input lncRNA sequences (or protein sequences) or upload a text file with FASTA-formatted lncRNA sequences (or protein sequences) for prediction, and freely download the results and visualize the predicted lncRNA-protein interactions. Moreover, gene ontology (GO) terms of proteins are annotated for indicating lncRNAs’ functions.

Fig 7 displays the top 10 predictions for the lncRNA “NONHSAT041930”. “NONHSAT041930” named OIP5-AS1 (OIP5 antisense RNA 1), is a mammalian lncRNA that is abundant in the cytoplasm [73]. OIP5-AS1 has gained wide attention. In 2011, it was first identified to be involved in brain and eye development [74]. In 2016, Kim et al.[75] found that it can prevent HuR binding to target mRNAs and thus suppress the HuR-elicited proliferative phenotypes. Moreover, the lncRNA was found to interact with GAK mRNA, promoting GAK mRNA decay and hence reducing GAK protein levels and lowering cell proliferation [76]. Among top 10 predicted proteins interacting with OIP5-AS1, two proteins have already been known to have interactions with OIP5-AS1, which are included in the NPInter dataset. In addition, we find evidence from literature to support other six predicted proteins. For example, IGF2BP1, IGF2BP2, IGF2BP3, EWSR1 and TIA1 have already been examined to interact with OIP5-AS1 according to lncRNA-protein interacting data report [77]. Protein Argonaute 2 (AGO2) is required for proper nuclear migration, pole cell formation, and cellularization during the early stages of embryonic development. Several studies [75,78] showed that OIP5-AS1 is associated with AGO2. Moreover, annotated GO terms of predicted proteins indicate the function of the lncRNA OIP5-AS1: mRNA binding (GO: 0005845, GO: 0035925, GO: 0036002, GO: 0048027, GO: 0098808) and cell proliferation (GO:0022013). More details are provided in S2 Table. These encouraging instances demonstrate that the proposed method can successfully predict novel lncRNA-protein interactions.

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Fig 7. Visualization of top 10 predicted interacting proteins for the lncRNA: “NONHSAT041930”.

Purple node stands for the lncRNA. Navy blue nodes indicate the predicted interacting proteins, and green nodes represent proteins that have observed interactions with the lncRNA. Moreover, we map the corresponding GO Terms (Orange nodes) of each interacting protein from QuickGO database (https://www.ebi.ac.uk/QuickGO/).

https://doi.org/10.1371/journal.pcbi.1006616.g007

Moreover, the server can predict interacting lncRNAs for proteins. For example, top 20 interacting lncRNAs of the protein “9606.ENSP00000240185” are shown in the Fig 8, and details are provided in S3 Table.

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Fig 8. Visualization of top 20 predicted interacting lncRNAs of the protein: 9606.ENSP00000240185.

Purple node stands for the protein. Navy blue nodes indicate the predicted interacting lncRNAs and green nodes represent lncRNAs that have observed interactions with the protein.

https://doi.org/10.1371/journal.pcbi.1006616.g008

Discussion

This paper presents a novel lncRNA-protein interaction prediction method, namely sequence-based feature projection ensemble learning (SFPEL-LPI). The novelty of SFPEL-LPI comes from integrating sequence-derived features and similarities with a feature projection ensemble learning frame. Specifically, SFPEL-LPI only utilizes lncRNA sequences, protein sequences and known interactions to extract features, and calculates lncRNA-lncRNA similarities and protein-protein similarities. Since sequences are usually available for lncRNAs or proteins, SFPEL-LPI can make predictions for almost all lncRNA-protein pairs. Moreover, diverse information leads to the good performances of SFPEL-LPI.

To evaluate the performance of SFPEL-LPI, an extensive set of experiments were performed on the benchmark dataset under three CV setting: CVlp, CVl and CVp, compared with state-of-the-art lncRNA-protein interaction prediction methods. The promising results validate efficacy of the proposed algorithm for predicting lncRNA-protein interactions, especially for the new lncRNAs or new proteins, which do not have known interactions. SFPEL-LPI outperforms five methods: RWR, LPBNI, KATZLGO, LPI-ETSLP, LPLNP, and makes 100.4%, 43.3%, 65.4%, 46.9%, 3.1% improvements in terms of AUPR scores. Further, we also analyze the running time of SFPEL-LPI and benchmark methods, and randomly perturb all known lncRNA-protein interactions to test the robustness of prediction methods. A web server is constructed to predict interacting proteins/lncRNAs for given lncRNAs/proteins. We adopt the lncRNA “NONHSAT041930” as an example to predict interacting proteins, and can find evidences to confirm novel lncRNA-protein interactions.

However, SFPEL-LPI still has several limitations. It has three parameters, and parameter tuning is time-consuming. In addition, known lncRNA-protein interactions are limited, and performances of SFPEL-LPI will be improved if more interactions are known.

Supporting information

S1 Table. Top 20 predictions of SFPEL-LPI and their ranks in predictions of benchmark methods.

https://doi.org/10.1371/journal.pcbi.1006616.s003

(DOCX)

S2 Table. Top 10 interacting proteins of LncRNA “NONHSAT041930” (OIP5-AS1) predicted by SFPEL-LPI.

https://doi.org/10.1371/journal.pcbi.1006616.s004

(DOCX)

S3 Table. Top 20 interacting lncRNAs of protein “9606.ENSP00000240185” (TAR DNA-binding protein 43) predicted by SFPEL-LPI.

https://doi.org/10.1371/journal.pcbi.1006616.s005

(DOCX)

References

  1. 1. Prensner JR, Chinnaiyan AM (2011) The emergence of lncRNAs in cancer biology. Cancer Discov 1: 391–407. pmid:22096659
  2. 2. Volders PJ, Helsens K, Wang X, Menten B, Martens L, et al. (2013) LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res 41: D246–251. pmid:23042674
  3. 3. Kung JT, Colognori D, Lee JT (2013) Long noncoding RNAs: past, present, and future. Genetics 193: 651–669. pmid:23463798
  4. 4. Pang KC, Frith MC, Mattick JS (2006) Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function. Trends Genet 22: 1–5. pmid:16290135
  5. 5. Kutter C, Watt S, Stefflova K, Wilson MD, Goncalves A, et al. (2012) Rapid turnover of long noncoding RNAs and the evolution of gene expression. PLoS Genet 8: e1002841. pmid:22844254
  6. 6. Fu M, Zou C, Pan L, Liang W, Qian H, et al. (2016) Long noncoding RNAs in digestive system cancers: Functional roles, molecular mechanisms, and clinical implications (Review). Oncol Rep 36: 1207–1218. pmid:27431376
  7. 7. St Laurent G 3rd, Wahlestedt C (2007) Noncoding RNAs: couplers of analog and digital information in nervous system function? Trends Neurosci 30: 612–621. pmid:17996312
  8. 8. Qu Z, Adelson DL (2012) Evolutionary conservation and functional roles of ncRNA. Front Genet 3: 205. pmid:23087702
  9. 9. Wang KC, Chang HY (2011) Molecular mechanisms of long noncoding RNAs. Mol Cell 43: 904–914. pmid:21925379
  10. 10. Muppirala UK, Honavar VG, Dobbs D (2011) Predicting RNA-protein interactions using only sequence information. BMC Bioinformatics 12: 489. pmid:22192482
  11. 11. Wang Y, Chen X, Liu ZP, Huang Q, Wang Y, et al. (2013) De novo prediction of RNA-protein interactions from sequence information. Mol Biosyst 9: 133–142. pmid:23138266
  12. 12. Suresh V, Liu L, Adjeroh D, Zhou X (2015) RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Res 43: 1370–1379. pmid:25609700
  13. 13. Xiao Y, Zhang J, Deng L (2017) Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks. Sci Rep 7: 3664. pmid:28623317
  14. 14. Lu Q, Ren S, Lu M, Zhang Y, Zhu D, et al. (2013) Computational prediction of associations between long non-coding RNAs and proteins. BMC Genomics 14: 651. pmid:24063787
  15. 15. Li A, Ge M, Zhang Y, Peng C, Wang M (2015) Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model. Biomed Res Int 2015: 671950. pmid:26839884
  16. 16. Yang JH, Li A, Ge MQ, Wang MH (2015) Prediction of interactions between lncRNA and protein by using relevance search in a heterogeneous lncRNA-protein network. 2015 34th Chinese Control Conference (Ccc): 8540–8544.
  17. 17. Wiggins BS, Saseen JJ, Page RL 2nd, Reed BN, Sneed K, et al. (2016) Recommendations for Management of Clinically Significant Drug-Drug Interactions With Statins and Select Agents Used in Patients With Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 134: e468–e495. pmid:27754879
  18. 18. Zheng XX, Tian K, Wang Y, Guan JH, Zhou SG (2016) Predicting lncRNA-Protein Interactions Based on Protein-Protein Similarity Network Fusion. Bioinformatics Research and Applications, Isbra 2016 9683: 321–322.
  19. 19. Zhang Z, Zhang J, Fan C, Tang Y, Deng L (2017) KATZLGO: Large-scale Prediction of LncRNA Functions by Using the KATZ Measure Based on Multiple Networks. IEEE/ACM Trans Comput Biol Bioinform.
  20. 20. Hu H, Zhu C, Ai H, Zhang L, Zhao J, et al. (2017) LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction. Mol Biosyst.
  21. 21. Zhang W, Qu Q, Zhang Y, Wang W (2018) The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions. Neurocomputing 273: 526–534.
  22. 22. Wei L, Tang J, Zou Q (2017) Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information. Information Sciences 384: 135–144.
  23. 23. Song L, Li D, Zeng X, Wu Y, Guo L, et al. (2014) nDNA-prot: identification of DNA-binding proteins based on unbalanced classification. BMC Bioinformatics 15: 298. pmid:25196432
  24. 24. Yuan J, Wu W, Xie C, Zhao G, Zhao Y, et al. (2014) NPInter v2.0: an updated database of ncRNA interactions. Nucleic Acids Res 42: D104–108. pmid:24217916
  25. 25. Gough J, Karplus K, Hughey R, Chothia C (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. Journal of Molecular Biology 313: 903–919. pmid:11697912
  26. 26. Zheng XX, Wang Y, Tian K, Zhou JG, Guan JH, et al. (2017) Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions. Bmc Bioinformatics 18.
  27. 27. Junge A, Refsgaard JC, Garde C, Pan XY, Santos A, et al. (2017) RAIN: RNA-protein Association and Interaction Networks. Database-the Journal Of Biological Databases And Curation.
  28. 28. Cheng ZZ, Huang K, Wang Y, Liu H, Guan JH, et al. (2017) Selecting high-quality negative samples for effectively predicting protein-RNA interactions. Bmc Systems Biology 11.
  29. 29. Pan XY, Fan YX, Yan JC, Shen HB (2016) IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. Bmc Genomics 17.
  30. 30. Zhang W, Liu J, Zhao M, Li Q (2012) Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features. International Journal of Data Mining and Bioinformatics 6: 557–569. pmid:23155782
  31. 31. Zhang W, Niu Y, Zou H, Luo L, Liu Q, et al. (2015) Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS One 10: e0128194. pmid:26020952
  32. 32. Zhang W, Chen Y, Liu F, Luo F, Tian G, et al. (2017) Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics 18: 18. pmid:28056782
  33. 33. Zhang W, Yue X, Chen Y, Lin W, Li B, et al. Predicting drug-disease associations based on the known association bipartite network; 2017. pp. 503–509.
  34. 34. Zhang W, Yue X, Liu F, Chen Y, Tu S, et al. (2017) A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Systems Biology 11: 101. pmid:29297371
  35. 35. Li D, Luo L, Zhang W, Liu F, Luo F (2016) A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinformatics 17: 329. pmid:27578422
  36. 36. Luo L, Li D, Zhang W, Tu S, Zhu X, et al. (2016) Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features. PLOS ONE 11.
  37. 37. Liu B, Liu F, Wang X, Chen J, Fang L, et al. (2015) Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43: W65–71. pmid:25958395
  38. 38. Liu B (2017) BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform.
  39. 39. Liu B, Liu F, Fang L, Wang X, Chou KC (2016) repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics 291: 473–481. pmid:26085220
  40. 40. Liu YP, Wu HY, Yang X, Xu HQ, Li YC, et al. (2015) Association between thiopurine S-methyltransferase polymorphisms and thiopurine-induced adverse drug reactions in patients with inflammatory bowel disease: a meta-analysis. PLoS One 10: e0121745. pmid:25799415
  41. 41. Liu B, Fang L, Liu F, Wang X, Chou KC (2016) iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. J Biomol Struct Dyn 34: 223–235. pmid:25645238
  42. 42. Du PF, Zhao W, Miao YY, Wei LY, Wang L (2017) UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. Int J Mol Sci 18.
  43. 43. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41: e68. pmid:23303794
  44. 44. Chen W, Feng PM, Lin H, Chou KC (2014) iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int 2014: 623149. pmid:24967386
  45. 45. Dong C, Yuan YZ, Zhang FZ, Hua HL, Ye YN, et al. (2016) Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots. Mol Biosyst 12: 2893–2900. pmid:27410247
  46. 46. Zhang W, Shi J, Tang G, Wu W, Yue X, et al. Predicting small RNAs in bacteria via sequence learning ensemble method; 2017. IEEE. pp. 643–647.
  47. 47. Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43: 246–255. pmid:11288174
  48. 48. Lin H, Ding H (2011) Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol 269: 64–69. pmid:20969879
  49. 49. Liu B, Wang S, Wang X (2015) DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation. Sci Rep 5: 15479. pmid:26482832
  50. 50. Zhang W, Yue X, Huang F, Liu R, Chen Y, et al. (2018) Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods 145: 51–59. pmid:29879508
  51. 51. Smith TF, Waterman MS, Burks C (1985) The statistical distribution of nucleic acid similarities. Nucleic Acids Res 13: 645–656. pmid:3871073
  52. 52. Zeng J, Li D, Wu Y, Zou Q, Liu X (2016) An empirical study of features fusion techniques for protein-protein interaction prediction. Current Bioinformatics 11: 4–12.
  53. 53. Zhang W., Yang W., Lu X., Huang F., and Luo F., “The Bi-Direction Similarity Integration Method for Predicting Microbe-Disease Associations,” IEEE Access, vol. 6, pp. 38052–38061, 2018.
  54. 54. Zhang W, Chen YL, Tu SK, Liu F, Qu QL (2016) Drug side effect prediction through linear neighborhoods and multiple data source integration. 2016 Ieee International Conference on Bioinformatics and Biomedicine (Bibm): 427–434.
  55. 55. Zhang W, Liu F, Luo LQ, Zhang JX (2015) Predicting drug side effects by multi-label learning and ensemble learning. Bmc Bioinformatics 16.
  56. 56. Zhang W., Xiong Y., Zhao M., Zou H., Ye X., and Liu J., “Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature,” Bmc Bioinformatics, vol. 12, no. 1, pp. 341, 2011.
  57. 57. Zhang W., Niu Y., Xiong Y., Zhao M., Yu R., and Liu J., “Computational Prediction of Conformational B-Cell Epitopes from Antigen Primary Structures by Ensemble Learning,” Plos One, vol. 7, no. 8, pp. e43575, 2012.
  58. 58. Zhang W, Zou H, Luo LQ, Liu QC, Wu WJ, et al. (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173: 979–987.
  59. 59. Xu X, Shen F, Yang Y, Zhang D, Shen HT, et al. (2017) Matrix Tri-Factorization with Manifold Regularizations for Zero-Shot Learning. pp. 2007–2016.
  60. 60. Nie F, Wang X, Deng C, Huang H (2017) Learning A Structured Optimal Bipartite Graph for Co-Clustering. pp. 4132–4141.
  61. 61. Bai Z, Walker PB, Tschiffely AE, Wang F, Davidson I (2017) Unsupervised Network Discovery for Brain Imaging Data. pp. 55–64.
  62. 62. Cai D, He X, Han J, Huang TS (2011) Graph Regularized Nonnegative Matrix Factorization for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33: 1548–1560. pmid:21173440
  63. 63. Cai D, He X, Han J (2007) Spectral Regression: A Unified Approach for Sparse Subspace Learning. pp. 73–82.
  64. 64. Wang Y, Qi W, Zhang L, Ying Z, Sha O, et al. (2017) The novel targets of DL-3-n-butylphthalide predicted by similarity ensemble approach in combination with molecular docking study. Quant Imaging Med Surg 7: 532–536. pmid:29184765
  65. 65. Wang Z, Liang L, Yin Z, Lin J (2016) Improving chemical similarity ensemble approach in target prediction. J Cheminform 8: 20. pmid:27110288
  66. 66. Zhou B, Sun Q, Kong DX (2016) Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach. Oncotarget 7: 32394–32407. pmid:27083051
  67. 67. Chen B, McConnell KJ, Wale N, Wild DJ, Gifford EM (2011) Comparing bioassay response and similarity ensemble approaches to probing protein pharmacology. Bioinformatics 27: 3044–3049. pmid:21903625
  68. 68. Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning. Pittsburgh, Pennsylvania, USA: ACM. pp. 233–240.
  69. 69. Zhang W, Yue X, Lin W, Wu W, Liu R, et al. (2018) Predicting drug-disease associations by using similarity constrained matrix factorization. BMC bioinformatics 19: 233. pmid:29914348
  70. 70. Natarajan N, Dhillon IS (2014) Inductive matrix completion for predicting gene-disease associations. Bioinformatics 30: i60–68. pmid:24932006
  71. 71. Singh-Blom UM, Natarajan N, Tewari A, Woods JO, Dhillon IS, et al. (2013) Prediction and validation of gene-disease associations using methods inspired by social network analyses. PLoS One 8: e58977. pmid:23650495
  72. 72. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, et al. (2017) A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 8: 573. pmid:28924171
  73. 73. van Heesch S, van Iterson M, Jacobi J, Boymans S, Essers PB, et al. (2014) Extensive localization of long noncoding RNAs to the cytosol and mono- and polyribosomal complexes. Genome Biol 15: R6. pmid:24393600
  74. 74. Ulitsky I, Shkumatava A, Jan CH, Sive H, Bartel DP (2011) Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell 147: 1537–1550. pmid:22196729
  75. 75. Kim J, Abdelmohsen K, Yang X, De S, Grammatikakis I, et al. (2016) LncRNA OIP5-AS1/cyrano sponges RNA-binding protein HuR. Nucleic Acids Res 44: 2378–2392. pmid:26819413
  76. 76. Kim J, Noh JH, Lee SK, Munk R, Sharov A, et al. (2017) LncRNA OIP5-AS1/cyrano suppresses GAK expression to control mitosis. Oncotarget 8: 49409–49420. pmid:28472763
  77. 77. Chen M, Zhao H, Lind SB, Pettersson U (2016) Data on the expression of cellular lncRNAs in human adenovirus infected cells. Data Brief 8: 1263–1279. pmid:27547808
  78. 78. Liu X, Zheng J, Xue Y, Yu H, Gong W, et al. (2018) PIWIL3/OIP5-AS1/miR-367-3p/CEBPA feedback loop regulates the biological behavior of glioma cells. Theranostics 8: 1084–1105. pmid:29464001