Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% , 96.45% (precision), 89.62%  and 98.71% (accuracy), 98.54% , 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from  Most biological processes within a cell are induced by a variety of interactions among the proteins and protein–protein interactions can form the basis for understanding protein functions, communications and regulations. Due to their importance in the cell, many experiential and computational methods have been developed to identify various interactions and determine the aftermath of the interactions .High-throughput techniques, such as yeast-2-hybrid (Y2H) assay, Co-Immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are first utilized to judge protein–protein interactions. Unfortunately, experimental approaches hold many inherent disadvantages and constraints. One of the limitations is the lack of coverage and quality of the global protein–protein interaction networks . In addiAlthough machine learning methods can use training data to build models and make predictions based on the best fit model, there still have some inherent drawbacks in raw data processing. One is that the majority of computational methods is highly dependent on hand-designed features. Another limitation is that these carefully chosen features are derived primarily from single attribute , rather than directly from raw data. Considering the huge amount of validated protein–protein interaction sequences produced by high-throughput technique and urgent needs to change from hand-designed to data-driven features, representation learning which can discover effective feature, underlying patterns and inherent mappings is adopted for data representation and deep learning is used to accomplish the prediction task.In the machine learning community, representation learning and deep learning are now two popular techniques for feature generation, noise elimination and accurate prediction. Representation learning aims to automatically learn data representations from raw data, these representations can be exploited by downstream machine learning models to improve the task performance . Given aWord2vec is a successful word embedding technique in natural language processing . The tooThere are three main reasons why the integration of residue representation into a deep neural network (DNN) is suitable for protein–protein interaction prediction. First, the principle of Word2vec technique considers the effect of a single word on surrounding words, which is consistent with the influence of a residue in the protein sequence on the surrounding residues. Previous studies have concluded that interaction residues exhibit aggregation to some extent . It meanS.cerevisiae, Human and five species-specific protein–protein interaction datasets, which are collected from publicly available interacting protein databases (DIP) are used for performance evaluation. The S.cerevisiae core dataset  is choseRepresentation learning can discover informative representations in a self-taught manner. Deep learning architecture can utilize multiple hierarchical layers to extract effective features. Leveraging the advantages of representation learning and deep learning, we proposed DeepFE-PPI framework, which consists of two main steps : (1) resWord2vec has been a very successful technique for representation learning in an unsupervised way. Word representations can reflect item meaning using the co-occurrence information of words in a corpus. The fixed dimensionality representations generated by Word2vec have been shown to be very useful for various NLP task. Except for processing words in text processing field, Word2vec can map other information units  to a low-dimensional implicit space. In this implicit space, the representation of each information element is a dense eigenvector. n. The larger n, the larger vocabulary size in the corpus. More vocabularies require more run time and memory consumption when training the Word2vec model. So, we set n as 1, which means that a residue is treated as a word and a protein sequence is treated a sentence. Compared to other protein representation methods, (1) this method can obtain different residue interactions from the original protein corpus. (2) the cost of run time and memory consumption is acceptable.This motivates us to transform each residue into a fixed dimensionality eigenvector representation. In previous work, many common word definition and splitting approaches in bioinformatics are proposed . TsubakiIn the training process of word representation, a large corpus of sentences should be fed into the model to ensure sufficient contexts are observed. In order to derive the representation of different biophysical and biochemical properties that hidden in protein sequences, 558,590 protein sequences from Swiss-Prot database are adopted as the input of the Word2vec model. The protein sequences were downloaded in Nov. 2018. There are two available models in Word2vec: the continuous bag of word (CBOW) model and the Skip-gram (SG) model. According to previous studies , it is fIn this paper, Word2vec has been implemented using Gensim library. Hyper-parameters should be decided in the training stage. Hyper-parameters selection is a task dependent on choice and can vary from task to task. In the Skip-gram model, the target words are the words in the context, size denotes residue dimension, window denotes the maximum distance between the current and predicted word, min-count means the number of the ignored words with total frequency lower than a fixed value. Since the Swiss-Prot database just contains 25 different type residues, so the value of min-count in our experiment is set as 0. Parameters size and window are considered as the decisive parameters in the training of the Skip-gram model. After comparison of different parameter combinations, the value of size is set to be 20 and window is set as 4. The specific Word2vec parameters selection process is shown in Section Parameter selection of Results. Deep learning supports highly flexible architectures, such as DBNs (Deep Belief Networks), CNNs , RNNs  and so on. Different deep learning architectures are suitable for different specific applications. For example, DBNs are good at analyzing internal correlations in high-dimensional data, CNNs are capable of capturing complex features, and RNNs are suitable for the analysis of sequential information . Considem. As a result, every protein sequence is represented as a vector of size D∗m (D is residue dimension and m is the fixed number of residues). If the length of a protein sequence is shorter than m, zero is appended to the short eigenvector to ensure every protein sequence has the same shape. These two embedding vectors are taken as input data of deep learning model. Our deep learning framework is composed of two separate DNN modules to extract high-level features that hidden in original embedding vectors and a joint module to integrate both DNN modules outputs to classify the input protein pair as interaction or non-interaction. The deep learning flowchart is shown in DNN is a very successful machine learning technique for classification in biology, but its input should be vectors with fixed dimension. This is a dreadful limitation because proteins are intrinsically expressed as sequences of arbitrary length. A simple measure is to uniform the length to a fixed size Generally speaking, we first use unsupervised representation learning method Res2vec to generate residue representations. Then each protein sequence is uniformed into a vector representation with the same length. After that, separate DNN is established for feature extracting, the merged layer is for feature integration and the last layer is for classification.In our study, all experiments are evaluated by cross validation and different metrics. For their introduction, please refer to the section  in the We perform the protein–protein interaction prediction by the derived Res2vec model for residue vectors generation and deep learning technology for classification, therefore, there have two parameter sets needed to be confirmed. Previous literature  has showIn the Skip-gram model, the two parameters of window size and residue dimension are most important. As the propensity for protein interactions is not a local property of single amino acid positions, it is expected that using the characteristics and patterns of neighboring residues could be helpful in the prediction of protein interactions. Previous studies  analyzedFor deep learning, prediction results are determined by input data and model complexity. The deeper the DNN network is, the more abstract the features learned and the more accurate the model achieves. The selection of the network depth is related to the specific application and experimental data, so the appropriate network depth should be determined by experimental verification.In the experiment, protein length is also an important factor that could influence the final result. We have experimented four different values of protein length starting from the average length with n step size as 100. The range of protein length is . So we discuss four different network structure depths in combination with different network inputs. The networks are incremented by one layer of fully connected layers. Hyper parameters are often selected in a statistically rigorous way via validation or cross-validation examples that are held out from training. We used five-fold cross-validation  and trained a neural network with four hidden layers on the S. Cerevisiae Core dataset. Usually, the training set is used to train models with different hyper-parameters, which are then assessed on the validation set. The best model configuration is then assessed by comparing the often used statistical measures, such as accuracy, recall, specificity, precision and MCC, on the test set.In order to evaluate the prediction performance of DeepFE-PPI, we compared the results of the proposed method with six state-of-the-art algorithms (Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), Native Bayesian (NB),  and Logistic Regression (LR)). The experiments are carried on the S. Cerevisiae Core dataset using five-fold cross validation procedure. From methods in The proposed deep learning method and the other six different classification results are listed in From Our analysis first proceeds on the S. Cerevisiae Core dataset. The results of different methods with five-fold cross validation on the S. Cerevisiae Core dataset are shown in Consequently, human dataset is employed as a test dataset. The average prediction results with five-fold cross-validation over twelve different approaches are given in To further validate the performance of our proposed method, we also compare the predictive performance of our method on five other independent species datasets. In this experiment, the selected 11,188 samples of S. cerevisiae core subset are used as training data, five other species datasets  are used to assess the performance of DeepFE-PPI. Typical cross-validation for pair-input methods divide available data into a training set and a test set, ignoring that the way of test datasets constructed can significantly impact the performance of the pair-input predicting programs . Based oDue to the input sequence representation determines the regulatory properties of each protein. Hence, it is critical to determine the optimal dimension of each residue for protein–protein interaction prediction. Maintaining a consistent data split ratio with five-fold cross validation, 72% of the dataset is split as a training set, 8% of the dataset is split as a validation set and 20% for model testing. We use DeepFE-PPI model to predict interactions on the S. Cerevisiae Core dataset and obtain 6 performance results under 6 residue eigenvector sets. Each result corresponds to a special residue dimension (from 4 to 24 with 4 steps). The performance with different residue dimensions is shown in To visualize the effect of residue dimension for residue quality, we applied principal component analysis on the optimal dimension residues. The top five ratios of the variance of each PCs are 0.6421, 0.1412, 0.0649, 0.0347, 0.0279. The first two principal components are adopted to cluster 25 residues. As can be seen from Protein–protein interaction prediction has been studied for many years. The main techniques used in this study is Res2vec for feature representation and deep learning for classification. The prediction results on different datasets indicate that Res2vec does as well or better than other complex feature extraction approaches and DeepFE-PPI has a certain help in protein–protein interaction prediction problem.The key to DeepFE-PPI framework is the use of Word2vec, which is capable of generating continuously sequence representation. Different residue representations generated by Res2vec might induce the following deep learning network to produce particularly poor or good results. Choosing an appropriate residue dimension is the key but we have not found an effective chosen method yet. In the parameter analysis section, we try to use principal component analysis to find the relationships between residues. Unfortunately, we only find that the common 20 residues cluster together while the other five uncommon residues are far away. From this phenomenon, we then choose the optimal residue dimension by evaluating the performance of the classifier as a criterion, not from the feature generator Word2vec. Based on this, there is a lot of work that can be done about how to choose the optimal residue dimension and why the selected dimension can induce the best classification for protein interaction prediction problem.Protein–protein interaction prediction is important for understanding the activity of complex cells from a molecular point of view. In this work, we propose a new protein sequence representation method combined with an effective deep learning framework to predict protein–protein interactions. Inspired by word embedding in natural language processing and leveraging large scale positive protein data, the proposed residue representation method Res2vec captures a diverse range of meaningful properties and represent each type residue as a low-dimensional expressive vector from raw data solely on protein sequence information. Then two individual DBN modules are used to extract high-level feature from each protein and a joint module to identify whether two proteins interact with each other or not. The results show that the combination of effective residue representation strategy and powerful deep learning technique is particularly useful for protein–protein interaction prediction. Comparisons with other published classifiers show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and an effective deep learning framework in an acceptable level of accuracy.10.7717/peerj.7126/supp-1Supplemental Information 1Continuous Skip-gram Model, Deep neural network, Cross validation, Performance evaluation, Memory and Time.Click here for additional data file.


With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at  Protein-protein interactions (PPIs) are useful for elucidating the changing mechanisms of organisms in physiological or pathological conditions and are important for disease prevention and drug development. In the last decade, numerous methods for studying protein-protein interactions, such as yeast two-hybrid screens , hybrid In recent years, a large amount of protein sequence information has been accumulated, and numerous computer calculation methods and sequence-based methods have become more universal and acceptable –18, suchThough the various methods described above for protein coding methods are useful, one of the drawbacks is that the order relationship of the entire amino acid sequence is not considered. To overcome this problem, we propose a sequence-based method based on a novel representation of the matrix of sequence (MOS). The MOS descriptor is first classified into 7 classes according to the successful use of classification in Shen et al. . Then, wPan et al. N×N.Matrix of sequence (MOS): Hypothetical non-empty finite set: Ω = {Based on the definition of the sequence matrix, the sum of all elements in the sequence matrix is equal to w1, …, WN}, where N is the number of categories of the sequence. Given sequence: S = S1, S2, …, SL, where L represents the length of sequence S, Si ∈ Ω, 1≤i≤L. The sequence matrix of a given sequence S can be expressed as:Hypothetical non-empty finite set: Ω = {S1, S2, …, SL;Input sequence: S = mij]N×N.Output sequence matrix: MOS =  ← VOS[si] + 1.Step 2. VOS[si] ← MOS[si] + VOS.Step 3. MOS, the learning rate was 0.001, the number of times to repeat the hold-out-validation was 30 and the number of times was 10,000 per iteration.The results of each prediction model are shown in The training time is related to the parameters, such as the width and depth of the model. To compare the training time of each code, we set the parameters the same. The parameters of DNN-MOS, DNN-CT, DNN-AC and DNN-LD are set as follows: the number of hidden layer nodes was set to 64, the activation function was ReLU, the optimization algorithm was Adam, the batch size was 128, the dropout was 0, the learning rate was 0.001, the number of times to repeat the hold-out-validation was 30 and the number of times was 10,000 per iteration. The results of the training time are shown in To further assess the practical prediction ability of DNN-MOS, we trained the models of DNN-MOS, DNN-CT and DNN-AC on a non-redundant dataset (removing the samples that has ≥25% sequence identity to any sample in the pre-training set). The prediction results are shown in In order to verify the effectiveness of the feature extraction method of MOS on PPIs, we combined the MOS with Decision Tree (DT), K-Neighbors (KN) and Random Forest (RF) on human data to construct three models of DT-MOS (decision tree—matrix of sequence), KN-MOS (K-Neighbors—matrix of sequence) and RF-MOS (random forest—matrix of sequence). The results are shown in We have presented a novel protein sequence coding approach for PPIs prediction. Of note, we propose a strategy for projecting protein sequences into a vector space, which is used to represent the matrix space of PPI information. Specifically, we first classify 20 amino acids into 7 amino acids according to their physicochemical properties . The dimSaccharomyces cerevisiae and human SIPs datasets, respectively. Wang et al. [H. pylori, and independent datasets, respectively. Li et al. [Recently, new feature extraction approaches for PPIs have been developed –33. Among et al.  reportedi et al.  developeWith the increasing number of PPI calculation methods, the coding methods of various amino acid feature vectors are also emerging. Although the various protein encoding methods such as AC, CT, and LD are useful, one of the disadvantages is that the order relationship of the entire amino acid sequence is not considered. The CT  considerS1 FileThere are 36,630 protein-protein pairs from total 9476 proteins, and the first column is protein ID from HPRD, the second column is the other protein ID and the two proteins constitute the positive Protein-protein interaction.(DOC)Click here for additional data file.S2 FileThere are 36,480 protein-protein pairs from total 2184 proteins, and the first column is protein ID, the second column is the other protein ID and the two proteins constitute the negative Protein-protein interaction.(DOC)Click here for additional data file.S3 FileThere are 3899 protein-protein pairs from total 2502 proteins, and the first column is protein ID from HPRD, the second column is the other protein ID and the two proteins constitute the positive Protein-protein interaction and protein identity of all the proteins from S3 file is below 25%.(DOC)Click here for additional data file.S4 FileThere are 4262 protein-protein pairs from total 661 proteins, and the first column is protein ID from HPRD, the second column is the other protein ID and the two proteins constitute the positive Protein-protein interaction and protein identity of all the proteins from S4 file is below 25%.(DOC)Click here for additional data file.


Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs.In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins.http://denglab.org/PLIPCOM/.The source code and supporting files are publicly available at:  Long non-coding RNAs (lncRNAs) have been intensively investigated in recent years , 2, and in silico methods are appealing for characterization of the lncRNAs that are less experimentally covered due to technical challenge [Although high-throughput methods for characterization of protein-RNA interactions have been developed , 9, in shallenge . One comhallenge  developehallenge  used thehallenge  to develhallenge  presenteIn recent years, network-based methods have widely been used to predict lncRNA functions , 15. ManIn this paper, we introduced an computational approach for protein-lncRNA interaction prediction, referred to as PLIPCOM, based on protein-lncRNA heterogeneous network. The heterogeneous network is constructed from three subnetworks, namely protein-protein interaction network, protein-lncRNA association network and lncRNA co-expression network. PLIPCOM incorporates (i) low dimensional diffusion features calculated using random walks with restart (RWR) and a dimension reduction approach (SVD), and (ii) HeteSim features obtained by computing the numbers of different paths from protein to lncRNA in the heterogeneous network. The final prediction model is based on the Gradient Tree Boosting (GTB) algorithm using the two groups of network features. We compared our method to both traditional classifiers and existing prediction methods on multiple datasets, the performance comparison results have shown that our method obtained state-of-the-art performance in predicting protein-lncRNA interactions.It is worth noting that we have substantially extended and improved our preliminary work published on the BIBM2017 conference proceeding . The impAs shown in Fig. All human lncRNA genes and protein-coding genes were obtained from GENCODE database   is the integrative correlation coefficient between lncRNA l and protein-coding gene p, Cd represents the correlation coefficient between l and p in dataset d, and D is the number of data sets. In particular, we take into account the gene pairs whose correlation coefficient are positive, and discard those with negative correlation coefficients, as the mutual exclusion relationship indicates that protein is unlikely to interacting with the lncRNA.where An additional paired-end RNA-seq datasest including 19 human normal tissues are obtained from the Human Body Map 2 project (ArrayExpress accession E-MTAB-513) and another study (GEO accession no.GSE30554). Expression levels are calculated using Tophat and cufflinks, and the co-expressions of proteinlncRNA pairs are evaluated using Pearson’s correlation coefficients.Finally, we built a global heterogenous network by merging the three types of subnetworks . The resulting network has 36,225 nodes  and 2,339,152 edges after removal of edges wit similarity scores <0.5.G denote the adjacency matrix for the global network, and T represent the transition probability matrix. Each entry Tij holding the transition probability from node i to node j is computed as below The diffusion feature is a high-dimensional vector describing the topological properties of each node, which captures its relevance to all other nodes in the network. The network diffusion features can be calculated using random walk with restart (RWR) algorithm , 35 on tGij is equal to 1 if node i is connected to node j in the network, and 0 otherwise. The RWR process can be written as follows: in which α is the restart probability leveraging the importance of local and global topological information; Pt is a probability distribution whose i-th element represents the probability of node i being visited at step t. After enough number of iterations, RWR will converge so that Pt holds the stable diffusion distribution. If two nodes have similar diffusion states, they locate in similar situation within the global network with respect to other nodes. Since there are 36,225 nodes  in the network, each node has a 36,225-dimensional diffusion state.where P is factorized into the form as below: In view of excessively high-dimensional features are prone to noise interference and time-consuming in model training, we apply singular value decomposition (SVD) –38 to reΣ are the singular values of P, and the columns of U and V are the left-singular vectors and right-singular vectors of P, respectively. For a given number n of output dimensions, we assign the top n columns of Σ1/2V to xi, namely, where the diagonal entries of X is the derived low-dimensional feature matrix from the high-dimensional diffusion features. In this work we set n=500 according to previous study [where us study .L and P two kinds of nodes in a heterogenous network, (ALP)n∗m is an adjacent matrix, the normalization matrix of ALP with respect to the row vector is defined as The HeteSim score is a measure to estimate the correlation of a pair of nodes, and its value depends on the paths that connects the two nodes through a string of nodes in a graph . HeteSimThe reachable probability matrix n, and Pi belongs to any nodes in the heterogenous network.where The detailed calculation procedure can be found in our previous work . Here weBased on the derived diffusion and HeteSim features, we build a classifier using the gradient tree boosting (GTB)  algorithIn GTB algorithm, the decision function is initialized as: N is the number of protein-lncRNA pairs in the training dataset. The gradient tree boosting algorithm repeatedly constructs m different classification trees h,h,...,h, each of which is trained based on a subset of randomly extracted samples, and then constructs the following additive function Θm(x): where βm and αm are the weight and parameter vector of the m-th classification tree h. The loss function L) is defined as: in which y is the real class label and Θ(χ) is the decision function. Both βm and αm are iteratively optimized by grid search so that the loss function L) is minimized. Accordingly, we obtain the gradient tree boosting model where We use grid search strategy to select the optimal parameters of GTB with 10-fold cross-validation on the benchmark dataset. The optimal number of trees of the GTB is 600, and the selected depth of the trees is 13. The rest parameters are set to default values.We randomly select 2,000 protein-lncRNA interactions from the experimentally validated protein-lncRNA associations as positive examples, and randomly generated 2,000, 4,000, 10,000, 20,000 negative samples that are not included in all known associations. As a result, we build a standard training set with 2,000 positive and 2,000 negative samples, and other three unbalanced data sets with more negative samples than positive ones. The ratios of positive and negative samples are 1:1, 1:2, 1:5 and 1:10 in the four training sets, respectively.vs negative ratio 1:2, 1:5 and 1:10, respectively. Note that all the positive and negative samples in these test sets are independently chosen and excluded from the training set.For objective performance evaluation, an independent test set is built by randomly selecting 2,000 protein-lncRNA associations from the experimentally validated ones, plus 2,000 randomly generated negative samples. To be more realistic, we accordingly construct other three unbalanced test data sets with positive TP and FP represent the numbers of correctly predicted positive and negative samples, FP and FN represent the numbers of wrong predicted positive and negative samples, respectively. The AUC score is computed by varying the cutoff of the predicted scores from the smallest to the greatest value.We firstly evaluate the performance of our method using 10-fold cross-validation. The training set are randomly divided into ten set of roughly equal size subsets. Each subset is in turn used as the validation test data, and the remaining nine subsets are used as training data. The cross-validation process is repeated ten times, and the average performance measure over the ten folds are used for performance evaluation. We use multiple measures to evaluate the performance, including precision (PRE), recall (REC), F-score (FSC), accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). They are defined as below: To verify the effectiveness of the diffusion and HeteSim features in predicting protein-lncRNA interactions, we evaluate the predictive performance of the two feature groups alone and combination of them (combined features), on the standard training set. As shown in Fig. c and gamma g parameters are optimized to 512 and 0.00195, respectively. The number of trees used in the gradient tree boosting of PLIPCOM is set to 600, and the maximum tree depth is set to 13.Since our method is based on the gradient tree boosting algorithm, we compared our method to several widely used classifiers, including k-nearest neighbors algorithm (kNN) , random Table α=0.9, c=-15, d=log(9999) and the iteration number is set to 10. The parameters of LPIHN are set to their default values, i.e. γ=0.5, β=0.5 and δ=0.3. For RWR, the restart probability r is set to 0.5. The ROC curves are drawn using the true positive rate (TPR) vs. false positive rate (FPR) upon different thresholds of these prediction results. As shown in Fig. We compare PLIPCOM with four existing network-based prediction methods, including RWR , LPIHN , PRINCE We further compare PLIPCOM with the most recent method, PLPIHS, on four independent test sets. As other three existing methods  are network-based and can only predict interactions between the nodes included in the prebuilt network, they can not work on independent test set and thus excluded out. In fact, PLPIHS has been shown to outperform other three existing methods in our previous study  and the To further illustrate the effectiveness of the proposed method, We present three lncRNAs for case studies, including HOTAIRM1 (ensemble ID: ENSG00000233429), XIST (ensemble ID:ENSG00000229807) and HOTAIR (ensemble ID:ENSG00000228630). The HOTAIRM1 is a long non-coding RNA that plays a critical role in regulating alternative splicing of endogenous target genes, and is also a myeloid lineage-specific ncRNA in myelopoiesis . HOTAIRMα-subunit of the stimulatory G protein [In NPInter V3.0 , HOTAIRM protein . Indeed, protein . In addi protein . NFKB1 i protein , and its protein , EZH2 (p protein . These cIdentification of the associations between long non-coding RNAs (lncRNAs) and protein-coding genes is essential for understanding the functional mechanism of lncRNAs. In this work, we introduced a machine learning method, PLIPCOM, to predict protein-lncRNA interactions. The major idea of PLIPCOM is to take full advantage of the topological feature of lncRNA-protein heterogenous network. We first build a protein-lncRNA heterogeneous network by integrating a variety of biological networks including lncRNA-lncRNA co-expression network, protein-protein interaction network, and protein-lncRNA association network. Two categories of features, including diffusion features and HeteSim features, are extracted from the global heterogeneous network. Subsequently, we apply the gradient tree boosting (GTB) algorithm to train the protein-lncRNA interaction prediction model using the diffusion and HeteSim features. Cross validations and independent tests are conducted to evaluate the performance of our method in comparison with other state-of-the-art approaches. Experimental results show that PLIPCOM gains superior performance compared to other state-of-the-art methods.From our perspective, the superior performance of PLIPCOM benefits from at least three aspects: (i) diffusion features calculated using random walks with restart (RWR) on the protein-lncRNA heterogenous network, and the feature dimension is further reduced by applying singular value decomposition (SVD); (ii) HeteSim features obtained by computing the numbers of different paths from protein to lncRNA in the heterogenous network; and (iii) effective prediction model built by using the gradient tree boosting (GTB) algorithm. As far as our knowledge, we are the first to apply both diffusion and HeteSim features to predict protein-lncRNA interactions, although these two types features are regularly used in characterizing biological networks in previous works. As shown in our experimental results, diffusion and HeteSim features are complementary and their combination can further improve the predictive power. Moreover, compared to other classifiers, such as SVM and kNN, GTB used by PLIPCOM can not only achieve high prediction accuracy, but also select the feature of importance for identifying lncRNA-protein interactions.P=(E−(1−α)T)−1(αE)=αQ−1E, in which E is unit matrix, T is the transition probability matrix, α is the restart probability and Q is an n∗n sparse matrix (n is number of nodes in the network). The time complexity of calculating inverse matrix Q−1 is O(n3), and can be optimized by using Cholesky algorithm. From our previous work, we know that the time complexity of calculating HeteSim feature is O(kn), where k is the number of samples and n is the number of nodes. Note that these two network features can be calculated in parallel. Moreover, we use the truncated SVD to reduce the diffusion feature dimension so that the time of GTB training process is greatly reduced. As a result, the time complexity of the methodology of PLIPCOM is moderate, and can be scaled to large networks.The time complexity of our method depends mainly on the feature extraction procedure and GTB algorithm. The diffusion feature is calculated using RWR and its time complexity can be inferred from the equation Although PLIPCOM show effectiveness and promising predictive power, we think its performance can be further improved by adding protein sequence and structural information. In the near future, we will integrate sequence and structural features to promote the prediction of potential lncRNA-protein interactions.


Many nucleic acid–protein structures have been resolved, though quantitative structure-activity relationship remains unclear in many cases. Thrombin complexes with G-quadruplex aptamers are striking examples of a lack of any correlation between affinity, interface organization, and other common parameters. Here, we tested the hypothesis that affinity of the aptamer–protein complex is determined with the capacity of the interface to dissipate energy of binding. Description and detailed analysis of 63 nucleic acid–protein structures discriminated peculiarities of high-affinity nucleic acid–protein complexes. The size of the amino acid sidechain in the interface was demonstrated to be the most significant parameter that correlates with affinity of aptamers. This observation could be explained in terms of need of efficient energy transfer from interacting residues. Application of energy dissipation theory provided an illustrative tool for estimation of efficiency of aptamer–protein complexes. These results are of great importance for a design of efficient aptamers. Proteins are macromolecules with a variety of functions in living cells. Protein functioning is supported by an ability to bind target specifically substances. Moreover, some proteins and enzymes catalyze further chemical conversion of bound substances. Several decades of intensive study of proteins have yielded large databases of structures of proteins and their complexes, thermodynamics of binding and catalysis, as well as kinetic data ,2,3,4,5.A striking example of non-understanding of high affinity of nucleic acid–protein complexes has been reported recently . We analExpanding this specific dataset to all known nucleic acid aptamer–protein complexes, we also did not find a clear correlation between interface structure and aptamer affinity to the protein . These rWe made a further attempt to find a structure–affinity relationship for all nucleic acid–protein complexes available in the databases. The key processes that mediate energy transfer can be the following: (1) changes in H-bonds with water near the interface; (2) conformational rearrangements of the protein and the aptamer; and (3) redistribution of binding energy via residues with large sidechains that are located near the interface. In this work, amino acids from the interfaces have been thoroughly annotated and analyzed. (1)X-ray structures have a resolution less than 3 Å;(2)Only binary complexes  were chosen to minimize allosteric effects;(3)Apparent equilibrium constants for the complex formation are known.The dataset contained nucleic acid aptamer–protein complexes extracted from the Protein Data Bank  with theThirty-five complexes were analyzed, of which thirteen were with the same protein, human thrombin . b = RT × lnKd, where R is gas constant, and T is temperature of the binding assay.As for the nature of nucleic acid aptamers, DNA, RNA, modified DNA, and modified RNA aptamers were in the set. This set almost coincides with our previous work, where an explicit analysis of aptamer nature-affinity correlation was described . Kinetic(1)Annotation of the amino acids that participate in polar contacts;(2)Annotation of the amino acids located within 4 Å vicinity to atoms that participate in polar contacts;(3)Annotation of the amino acids located within 4 Å vicinity to nucleotides that form 3 or more polar contacts (putative “hot spots”).Several approaches were applied to analyze the interfaces of aptamer–protein complexes, having selected differences in amino acids. These include:All annotations are summarized in b means that large sidechains are much more common in complexes with high affinity. This effect cannot been attributed solely to high amount of aromatic amino acids (“lengths” parameters are in the range 7–10) or positively charged amino acids (“lengths” parameters are in the range 5–7), as is seen from correlation coefficients < 0.2 X-ray structures have a resolution less than 3 Å;(2)X-ray structure is for the whole protein, not a protein domain;(3)DNA has unmodified nucleotides only;(4)Apparent equilibrium constants for the complex are known.An interesting and well-studied object is complexes of HTH-type proteins with DNA double helixes. HTH-type proteins have a specific DNA binding motif: helix–turn–helix (HTH). They are a classical object of studying DNA binding and recognition. Therefore, comparing interfaces of complexes of proteins with artificial nucleic acid aptamers and natural DNA double helices is of great value. Criteria for this dataset were the following:b = RT lnKd, where R is gas constant, and T is temperature of the binding assay. For one of the proteins, fis, 18 complexes with different DNA duplexes were described, including optimal and non-optimal ones. All these complexes were analyzed in the same way as aptamer–protein complexes described above  . The sized above .b  [In extreme cases, such as in plant and algae photosystems, there are special proteins that mediate efficient dissipation of energy from the light-harvesting complexes ,42,43. T1/2) . As for 1/2) ,45, and 1/2) . Compari1/2) . y is the dissipated energy and Cy is an information transmitted. Shannon’s channel capacity equation describes the transmitted information (Cy) connected with the bandwidth of the channel (dspace), the dissipated energy (Py), and the thermal noise (Ny):B is Boltzmann constant and T is temperature. Thus, the efficiency of the molecular machine is as follows:SC and NAA are mean length of sidechain and number of amino acids in 4 Å proximity to polar contacts, correspondingly; LSCNAA is an analogue of the bandwidth of the channel from Equation (2). NPC is the number of polar contacts; EPC is energy of one polar contact; NPC∙EPC represents a rough estimation of the energy that is to be dissipated (Py). RT is a rough estimation of thermal noise (Ny), with R a gas constant and T as temperature. We used the following parameters: EPC = 6 kJ/mol  and T = 298 K. The parameter Cy′ reflects energy transfer by the protein part of the interface; this parameter was calculated for aptamer–protein and DNA helix–protein complexes. Cy’ values are listed in Direct experimental study of dissipation of energy in proteins without unusual prosthetic groups is complicated due to inability to trace specific residues only. However, bioinformatic analysis provided clues of a role of energy dissipation in protein functioning. A bridge between affinity and capacity for information transmission was paved for DNA–protein complexes ,49. The than 70% ,49. We ay′ is connected with −ΔGb. Changes in Gibbs free energy during binding can be represented as a sum of energy of polar contacts (Py = NPCEPC) and a summand W, which includes energy of dehydration, conformational rearrangement, and energy from other types of interactions:t = 70% according to [y′), if the summand W is equal for different complexes. The summand W includes all changes in energetic state of the molecule other than polar contacts, and this summand varies significantly. The example with different complexes of fis protein (2 = 0.97) for the most efficient complexes, and all other dots are located left of the line with the single outlier. The outlier is the complex of aptamer SL5 with its protein target (the dot is in right bottom part of the A question remains of how parameter Crding to ), for th protein B clearly protein A. Seven y′ versus −ΔGb it is easier to compare different types of complexes, as in this case data for G-quadruplex aptamers with thrombin, other aptamer–protein complexes and HTH-type proteins with DNA duplexes are in the same range of values. Here the most efficient complexes are assumed to dissipate the energy from polar contacts without energy-consuming conformational changes. In the examples with high affinity, nucleic acids provide a complimentary surface (large numbers of polar contacts) with an appropriate protein site (with large amino acids in the interface). Using the representation Cb value  or just a long substituent in thymine from the thymine pair (T4K) that has no contact with a protein but is exposed to the solvent  in SL4 and benzyl (7 carbon atoms) in SL5. The protein parts of the interfaces are the same, and aptamer conformations and thermal stability are the same [One more excellent example is the single outlier with the highest efficiency of the complex, aptamer SL5. The −ΔGthe same . The onlt to be 0.7 for those 7 complexes that are located on the right edge of the Cy′ vs. −ΔGb distribution. Using parameter Cy′ (Equation 5) and its dependence on −ΔGb (Equation 8), the theoretically achievable changes in Gibbs free energy can be calculated for all DNA-protein and aptamer–protein complexes:2 = 0.97). The efficiency of each complex was calculated by dividing experimental values of changes in Gibbs free energy (Besides graphical representation, numerical estimation of the efficiency of the complex can be used. Referring to 70% as the limit of efficiency of DNA binding proteins ,49, we aThe structures were uploaded from RCSB PDB  and procDetailed analysis and description of 63 protein structures discriminated peculiarities of high-affinity nucleic acid–protein complexes. The volume of the amino acid sidechain within the interface was demonstrated to be the most significant parameter that correlates with affinity of aptamers to proteins. This correlation could be explained in terms of need of efficient energy transfer. A parameter for estimation of the efficiency for nucleic acid–protein complexes was proposed. These results are of great interest both for understanding the fundamental principles of protein functioning and for design and improvement of efficient ligands, particularly nucleic acid aptamers.


Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension. Polycystic ovarian syndrome (PCOS) is a hormonal disorder that affects women in their reproductive years, and its cause remains unknown due to its heterogenic symptoms . PCOS paNovel insights into diseases and their relationships can be achieved through computational analysis on the integration of molecular data in comparing different diseases based on their shared interacting proteins. Diseases are classed as being comorbid based on the assumption that they share genetic components such as proteins and/or biological pathways . Shared Here, a similar approach was applied to investigate the association between PCOS and other diseases. We used the MCODE algorithm to identify highly connected regions in the PCOS PPI network that represent molecular complexes. These complexes were used to discover shared proteins and shared pathways between PCOS and its associated diseases.In total, 8185 PCOS-related proteins (PCOSrps) were used to build the PCOS PPI network. Overall. 20,277 interactions were established between 5213 PCOSrps. PPI information was obtained from the Human Integrated Protein–Protein Interaction Reference (HIPPIE) database. The remaining 2972 PCOSrps were excluded from the network due to the unavailability of their interaction partners and significant interactions calculated by HIPPIE. This dataset was used in the two-tier analysis, i.e., to search for diseases associated with PCOS using subnetworks (described as PCOS–disease subnetworks) and to identify all significant pathways in describing PCOS–disease associations .p-value < 0.01 calculated from the Fisher’s exact test  identified 77 PPI subnetworks in the PCOS network . Overallact test . The assSubnetwork 1 highlighted the association of PCOS with migraine, in which three PCOSrps were found in PCOS and migraine. These were 40S ribosomal proteins, namely RPS7, RPS10, and RPS26, which are involved in ribosome biogenesis.In PCOS–disease subnetwork 8, five PCOSrps were found for ovarian cancer, suggesting the association between PCOS and ovarian cancers. Those proteins were BRCA1 (breast cancer type 1 susceptibility protein), CDKN1B (cyclin-dependent kinase inhibitor 1B), PPP1CC , URI1 , and SKP2 (s-phase kinase-associated protein 2).Subnetworks 26 and 34 suggested the association between PCOS and schizophrenia with the identification/existence of four PCOSrps , HLA-C , HLA-E , and LILRB1 (leukocyte immunoglobulin-like receptor subfamily B member 1)) in subnetwork 26 and 27 PCOSrps in subnetwork 34. Further, these proteins were found in schizophrenic patients, as listed in DisGeNET . The PPIThe association between PCOS and hypertension was discovered from subnetwork 28 with the identification of five PCOSrps; i.e., BBS1 (Bardet–Biedl syndrome 1 protein), BBS2 (Bardet–Biedl syndrome 2 protein), JUN (transcription factor AP-1), MYH9 (myosin-9), and SMAD4 (mothers against decapentaplegic homolog 4), and all these are linked to hypertension. Significant subnetworks with shared PCOSrps are presented in Pathway enrichment analysis was performed to find pathways that are statistically involved in PCOS and its associated diseases. The significant pathway is referred to the same pathway involved in both PCOS and its associated diseases. Information on the PCOS–disease association and their interacting shared proteins provides the opportunity to recognize potentially interesting gene and protein candidates that can be used to investigate the genetic basis of PCOS.Subnetwork 1 was enriched with ribosomal proteins that are involved in ribosome biogenesis or protein translation . In subnFrom the pathway enrichment analysis, subnetwork 26 was enriched with proteins involved in an antigen processing and presentation pathway that consisted of three PCOSrps  shared between PCOS and schizophrenia .Meanwhile, autophagy, apoptosis, and necroptosis were identified as significant pathways in subnetwork 34, which refers to the association of PCOS with schizophrenia, depressive disorder, and obesity . The PCOp-value of <0.001, and they were also found from clinical observations on PCOS women [In this study, we successfully identified 17 diseases from 12 PCOS–disease subnetworks using the MCODE algorithm. The diseases were significantly related to PCOS based on the Fisher’s exact test calculated OS women . The assOS women ,4. This PCOSrps RPS7, RPS10, and RPS26 were found in PCOS–disease subnetwork 1. These proteins were categorized as shared proteins and might play a role in PCOS–migraine association. RPS7, RPS10, and RPS26 are ribosomal proteins that play a role in ribosome assembly and protein translation . This paBRCA1, CDKN1B, PPP1CC, SKP2, and URI1 were clustered in subnetwork 8 and categorized as shared proteins in PCOS and ovarian cancer, hence suggesting an association between the two. This subnetwork was enriched with the long-term potentiation pathway that is involved in synaptic efficacy and plays a role in learning and memory . Long-teThe association between PCOS and schizophrenia was identified in subnetwork 26 and subnetwork 34. The antigen processing and presentation pathway was significant in subnetwork 26, as the majority of the proteins in this subnetwork consist of HLA proteins, also known as major histocompatibility complex (MHC) proteins, which are main players in this pathway. This pathway was also involved in autoimmune diseases and immune reaction . This rePCOS–disease subnetwork 34 not only showed the association between PCOS and schizophrenia, but also displayed the association between PCOS, depressive disorder, and obesity. This finding suggests that schizophrenia, depressive disorder, and obesity can simultaneously occur in women with PCOS. This association exists if there is a perturbation in autophagy, which is one of the enriched pathways in subnetwork 34. Autophagy is a degradation process of cytoplasmic components mediated by lysosomes, involving four shared PCOSrps , GABARAP (gamma-aminobutyric acid receptor-associated protein), and SNAP29 ) with schizophrenia, three shared PCOSrps , and PRKACA ) with depressive disorder, and one shared PCOrp (AKT1) with obesity. Any interaction between PCOSrps involved in autophagy with AKT1, be it direct or indirect, is strongly associated with the development of these comorbidities. The autophagy-related genes were significantly downregulated in PCOS endometrial tissue . MeanwhiResults of the pathway enrichment analysis of PCOS–disease subnetwork 28 suggested the comorbidity between hypertension and PCOS, as both diseases shared the mitophagy pathway. Four PCOSrps were identified to be involved in mitophagy, i.e., E2F1 (transcription factor E2F1), FOXO3 (forkhead box protein O3), JUN, and MAPK9 (mitogen-activated protein kinase 9). Mitophagy is a process of damaged mitochondria removal via autophagy . MitochoFindings from this study demonstrate that the integration of human PPI networks with protein–disease information and pathway enrichment analysis of the PCOS–disease network can be used as a proof-of-principle, where only several subnetworks were discussed as examples to describe the association of PCOS with 17 diseases. Important shared proteins and pathways between PCOS and specific associated diseases were identified, and further experimental studies need to be carried out to validate the obtained information that will provide new insights into molecular mechanisms of PCOS. A better understanding of the pathophysiology of PCOS will be essential for the management of PCOS and its complications. Nonetheless, these novel relationships could offer new insights into disease etiology and classification, as well assisting several aspects such as biomarker development, drug target discovery, and diagnosis improvement.www.pcosbase.org) [www.disgenet.org) [PCOS-related proteins (PCOSrps) and their associated diseases were retrieved from PCOSBase  , a manuaase.org) , DISEASEase.org) , Diseasease.org) , GWAS Caase.org) , GWASdb ase.org) , MalaCarase.org) , Online ase.org) , Phenomiase.org) , and Thease.org)  and 31 gnet.org) .http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/) [A PCOS PPI network was constructed using a PCOSrp dataset obtained from PCOSBase, combined with the information on PCOSrps that were obtained from Human Integrated Protein–protein Interaction Reference (HIPPIE) (hippie/) . HIPPIE hippie/) .max = |V|(|V| − 1)/2, where V is the number of nodes in the subnetwork). Density and number of nodes are used to calculate the score of each subnetwork (score = density × number of nodes) [The MCODE algorithm , one of f nodes) . PPI subp-value < 0.01) associated with PCOS in every PPI subnetwork. This statistical significance test used the analysis of 2 x 2 contingency tables [Each protein in a PPI subnetwork was annotated with disease-associated information compiled from DisGeNET . Fisher’y tables ,61. The Comorbidity data obtained from  supporteThe biological function of the subnetworks were determined from the pathway enrichment analysis using ClueGO  against


Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research. A great quantity of studies has indicated that more than 90% of DNA is transcribed into RNA in human organism, the vast majority of which are non-coding RNA. Non-coding RNA (ncRNA) is a RNA that does not encode a protein, and plays a very broad regulatory role in many organisms' life activities. Abundant and functionally important types of non-coding RNAs include transfer RNA (tRNA) and ribosomal RNA (rRNA), and small RNAs such as microRNAs, siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs, and the long non-coding RNAs. Long non-coding RNA (lncRNA) refers to ncRNA longer than 200 nucleotides. LncRNA was originally considered a “noise” of genomic transcription, a byproduct of RNA polymerase II transcription, without biological function. But recent studies indicate lncRNA involves in a variety of important regulatory procedures, such as chromatin modification  Most of the models mentioned above don't use lncRNA-protein interactions data, but are trained using RNA-protein interactions data. This leads to a limitation on the ability to forecast the lncRNA-protein associations. (2) Some of the models utilize the NPInter . NONCODE  is connected with the protein p(j), Y(l(i), p(j)) is 1, otherwise 0. According to sequence similarity matrix, the interactions between lncRNAs and proteins are measured. We screen the lncRNAs and proteins sequences which are inferior quality or cannot find their corresponding proteins and lncRNAs. The inferior quality refers to incomplete sequence information and repeated lncRNA and protein sequences. Finally, 4158 high quality lncRNA-protein associations are obtained.To facilitate the description of lncRNA-protein interactions and the algorithmic model, matrix LS is built, where the empty LS(l(i), l(j)) indicates the sequence similarity between lncRNA l(i) and l(j). LS is normalized as below:In our work, we calculate the similarity of the lncRNA sequence according to the lncRNA sequence information. These lncRNAs sequences information is acquired from the NONCODE 4.0 database. As a result of filtering, we gain 990 credible lncRNAs sequences. The regularized Smith-Waterman algorithm Pearson,  is used sw(l(i), l(j)) is the sequence similarity between lncRNA l(i) and l(j) calculated according to the Smith-Waterman algorithm.Where PS, in which the entity PS(p(i), p(j)) expresses the sequence similarity between protein p(i) and p(j). The PS is normalized as below:We screen 27 dependable protein sequences on the basis of the lncRNA-protein network, they come from Uniprot , p(j)) is the sequence similarity between protein p(i) and p(j) calculated according to the Smith-Waterman algorithm.Where The workflow of our IRWNRLPI model is given in Figure SR is finally obtained. The right box is the process of adjacency regularization, and finally the lncRNA-protein score matrix SN is obtained. The bottom of Figure S by integrating the above two methods.The flowchart of this section is given in the Figure p, the process of predicting the lncRNAs associated with p is modeled as a random walk on the weighted graph G. The process can be roughly divided into four steps.In random walk model, given a protein p, the known lncRNAs associated with p and the candidate lncRNAs associated with p and their relations form a network, expressed as a weighted graph G . Each vertex (v ∈ V) represents the lncRNA or candidate lncRNA associated with p. Each edge (e ∈ E) denotes the relationship between the two vertices connected by edge e. We denote sequence similarity between vx and vy as Sim , and the weight w of edge e is Sim . The greater the w, the more likely that the two vertices are correlated with a set of similar proteins. In this network, the known lncRNA associated with p is called a labeled node. The remaining lncRNAs have so far, no evidence that they are related to p, which are unlabeled nodes.In the first step, the lncRNA network is established based on the sequence similarity between lncRNAs. For a given protein R to establish two one-step transition matrices LQ and LU. First of all, we construct the correlation matrix R. For vi, we evaluate the extent of relevance between neighbors vj and p, which is denoted by rij. Firstly, suppose that the set of all the labeled nodes is denoted as Q, vi ∈ Q. If vi is relevant to protein p, its neighbors may also be relevant to p. In addition, when vi is a labled node, the association probability is greater than the association probability when vi is an unlabeled node. Thus, the former and the latter are multiplied by wQ ∈  and wU ∈  separately. Evidently, wQ is higher than wU. Secondly, suppose U is the set of all unmarked nodes, which may be associated with lncRNAs, and vi ∈ U. If vi is related to p, its neighbors may also be associated with p. The weight of the associated information from the unmarked node is wU. Thirdly, if the two lncRNAs are not connected, such as vi and vj, rij is set to 0. Finally, an lncRNA to a value of itself is set to 0.In the second step, constructing the correlation matrix R (rij) M×M is constructed on the basis of the above rules, rij is formally defined as follows:vi is the vertex and vj is one of its neighbors.In which L (lij) M×M. We proportionate the transfer probability lij to rij. The matrix R is normalized by the next type, and the one step transfer probability array L (lij) M×M is obtained:Then, we construct the transfer matrix lij indicates the transition possibility from vi to vj. Nevertheless, after the row of R is normalized, the weights (wQ and wU) for distinguishing between the labeled node and the unlabeled node associated information are lost, thus ignoring the effect of the previous information about whether the vertex is relevant to p. In order to settle the difficulty, we divide the matrix L into two arrays of LQ and LU. LQ expresses the transformation array of the marked node, and LU indicates the transfer matrix of the unmarked node. All lines of the marked (unmarked) node in LQ (LU) are in accordance with the relevant rows in L, the rest of rows of LQ (LU) are set to 0.p, that is, estimate the correlation score of the candidate lncRNAs. In view of the transfer matrix LQ and LU, the prediction method is further established as below:In the third step, a new forecast method on account of random walk is established to evaluate the correlation scores between each unmarked node and S(t + 1) represents a probability vector, indicating the probability that the walker reaches the ith vertex at time t + 1 is Si(t+1). The walker begins with the marked node, the components in S(0) represent the original probability, which means the walker begins at the same probability at time 0 from a marked node. And Si(0) calculates according to the following formula:First, rQ and rU  to the labeled node and the unlabeled node, respectively. In fact, rQ and rU replace the ignored function of wQ and wU. Finally, when the walker finds a marked node, at time t+1 it will go back the initial vertex (marked node) at probability pQ(1-rQ) and start walking again. The probability total of the walkers arriving at each marked node at time t is expressed as pQ. The formula is as follows:Second, to use priori information, we assign weights pU(1-rU). The probability total of the walkers arriving at each unmarked node at time t is expressed as pU, it is equal to 1-pQ. X defines the nodes at which the walker returns and restarts. Since walker begins with a marked node, X is equal to S(0).Likewise, when the walker finds an unmarked node, at the next time it will return to the beginning vertex with possibility L1-norm between S(t) and S(t + 1) less than 10−10. The definition of the correlation fraction of unmarked nodes is the steady state probability of the pedestrians staying at that vertex. In this way, all unmarked nodes get a correlation score, and we sort them according to their fractions. The greater the fraction, the more likely that the unlabeled node is associated with the given protein p. The score matrix obtain by this part is denoted by SR, in which SR(l(i), p(j)) is the possibility of association between lncRNA l(i) and protein p(j).The fourth step is to sort all unmarked nodes and choose potential candidates. The walker begins with the marked node and starts iterating. When the iteration satisfies the condition of convergence, the iteration procedure suspends. The convergence condition is r < < min . li and protein pj, separately. The following formula is used to calculate the probability of association pij of the lncRNA-protein pair :Here we explain the neighborhood regularized logistic matrix factorization method. First, lncRNAs and proteins are mapped to shared potential spaces with dimension r, and U ∈ ℝm×r and V ∈ ℝn×r to represent the set of potential vectors for all lncRNAs and all proteins.In order to simplify, we utilize In order to make our modeling more efficient and more accurate for lncRNA-protein interactions prediction, we recommend giving positive samples a higher level of importance than negative samples Johnson, , the weiSuppose all samples are trained independently, and the probability as follows:yij = 1, c(1−yij) = 1−yij, when yij = 0, cyij = yij. So, we rewrite the formula (7) as follows:Note that when In addition, we will carry out zero mean spherical Gaussian priori on the potential vector of lncRNA and protein:Among them, Thus, the posterior distribution logarithm is as below:C is an absolute term. Maximizing the posterior distribution is same as minimizing the below object functions:Where F show the Frobenius norm of the array. Alternating gradient descent method  ∈ L\li, where N(li) makes up selecting the K1 most similar lncRNAs of li. After that, we structure the set N(pj) ∈ P\pj, which is made up of the K1 most similar proteins of pj. In the experiment, we set K1 to 5 according to experience.By mapping lncRNAs and proteins to shared potential space, the logistic matrix factorization method can effectually evaluate the monolithic structure of lncRNA-protein interactions information. In addition, we use lncRNAs and proteins neighbors to further advance the forecast veracity. For lncRNA A, and aiμ is defined as below:Here, the lncRNA neighborhood information can be represented by the adjacency array B, and bjv is defined as below:The protein neighborhood information is described by the adjacency matrix A and B are asymmetric.It should be noted that matrix li and its nearest neighbor N(li) in the potential space, which can be gained by minimizing the below object functions:The main idea of predicting lncRNA-protein interactions with lncRNAs neighborhoods information is to minimize the distance between Dl and Among them, tr(•) is matrix trace, Wherein, K1 the nearest neighborhoods, the final prediction model can be derived. By substituting Equations  into Equation (12), the resulting model is as follows:By taking into account lncRNA-protein associations and lncRNAs and proteins L, the gradient relative to U and V as below:An alternating gradient rise process can resolve the optimization problem in Equation (17), which is represented as P ∈ Rm×n, and pij (see Equation 1) represents the Hadamard product of the two arrays. In order to quicken the constriction of the gradient decline optimization method, we utilize the AdaGrad algorithm to adaptively select the grad step length.U and V are known, the association probability of any unknown lncRNA-protein pair  can be forecasted by formula (6). The negative dataset L−and P−of lncRNAs and proteins might influence on lncRNA−protein interactions. The set of K2 nearest neighbors in L+ and P+ are denoted as N+(li) and N+(pj) for lncRNA N+(li) and N+(pj) are structured utilizing the same standard as utilized to structure neighborhoods during the training procedure. Then, the interaction probability between lncRNA ui and protein vj is modified to:If potential carriers whereK2 is set to 5 based on experience. The score matrix obtained by this part is denoted by SN, and SN(l(i), p(j)) is the possibility of association between lncRNA l(i) and protein p(j).Note that Equation (21) shows a general case of smooth learning lncRNA specificity and target-specific potential carriers. In our experiment, SR and SN, and then take the average. The final fraction array is denoted as S, and S(l(i), p(j)) is the possibility of association between lncRNA l(i) and protein p(j). The formula is as follows:At last, to avoid the unsatisfactory result of using one of the two methods alone, we adopt an integration strategy and propose the integration model IRWNRLPI. Here we combine the two algorithms of random walk and neighborhood regularized logistic matrix factorization, and obtain a desired result. The specific approach is that we use these two algorithms obtain two score matrix In this work, to measure the capability of our IRWNRLP model, we perform LOOCV on lncRNA-protein interactions that have been experimentally verified. In the LOOCV experiment, it is assumed that a total of N samples, one of them is selected as a test sample, and the rest of the samples are selected as training samples. So, we result in N classifiers, N test results, and we will utilize the average of the N results to evaluate the capability of our method. Use the LOOCV to obtain the receiver operator characteristics (ROC) curve and calculate the area under ROC curve (AUC). AUC is an important popular metric for evaluating the classification model. If AUC = 1, IRWNRLPI has perfect performance; if AUC = 0.5, it represents random performance. There is also a popular indicator the area under prediction recall curve (AUPR), it is more adaptive for category unbalanced datasets because it penalizes false positives more in the assessment. Because of the presence of massive unknown labeled data in the dataset, AUPR is used to lessen the impact of misinformation for false positives on the function of the prediction model. The larger the value of AUPR, the better the capability of the method.For adequately examining the capability of the method, we introduce the following indicators to evaluate our method: ACC , SEN (sensibility), PRE (precision), and F1 (F1-scores), these indices are extensively utilized in bioinformatics, remarked as  can reflect if the classification model is robust. F1 is 1 for perfect method, while the worst model of F1 is 0.In this part, we compare IRWNRLPI with other four models on NPInter v2.0, which are LPI-ETSLP, RWR, LPBNI, and RPISeq. Among them, RPISeq is compared with IRWNRLPI as an example of the machine learning model, in view of RF and SVM classifiers. The other three methods, LPI-ETSLP, RWR, and LPBNI, forecast potential correlations with IRWNRLPI using identical type of lncRNA and protein sequences information. The results of IRWNRLPI and the other four models are displayed in Figure We perform all of these models on the same dataset, and implement LOOCV experiments to compare their performance. As shown in Figure From the indicators in Table To evaluate the capability of the prediction method more comprehensively, we use IRWNRLPI to forecast potential lncRNA-protein interactions in view of the known associations of “Mus musculus” in the NPInter v3.0 dataset. The top 10 lncRNA-protein interactions are displayed in Table LncRNA involves a variety of important cellular regulatory processes and many disease progression processes, particularly in the development of various cancers. In general, most lncRNAs play their function by interacting with the corresponding RNA-binding proteins. Therefore, predicting the new lncRNA-protein associations is conducive to the research of lncRNA. Nevertheless, lncRNA-protein interactions experiments will cost a lot of materials, human and financial resources. Therefore, the utilization of computational methods to forecast lncRNA-protein associations arouses widespread concern. In our work, to obtain better prediction results, we introduce the idea of integrating algorithm and present the IRWNRLPI method, which integrates two prediction methods, random walk and neighborhood regularized logistic matrix factorization, to forecast lncRNA-protein interactions. IRWNRLPI bases only on experimentally validated lncRNA-protein associations, which avoids dependence on negative sample datasets. We conduct a more comprehensive evaluation of IRWNRLPI, test our model in the NPInter v2.0 dataset, and compare it with other four methods. In the LOOCV experiment, the AUC value of IRWNRLPI is 0.9150, indicating that IRWNRLPI performs well in the forecast of lncRNA-protein correlations. And IRWNRLPI obtains the AUPR value of 0.7138, which states clearly the responsibility of this method. In addition, we use the “Mus musculus” dataset as a case study to test IRWNRLPI and investigate the practical capability of this method in forecasting unknown lncRNA-protein associations. Case study shows that IRWNRLPI is able to forecast other new lncRNA-protein interactions. With the continuous progress of science and technology, more and more lncRNA-protein interactions will be found, and then the accuracy of IRWNRLPI prediction will also increase. In conclusion, IRWNRLPI is an efficient model of predicting potential lncRNA-protein associations, and we also hope that IRWNRLPI can be used in a wider range of studies.The excellent and reliable predictive performance of IRWNRLPI is mainly attributable to the following factors. Firstly, unlike the traditional machine learning methods, IRWNRLPI uses semi-supervised learning to derive unknown information primarily through known associations and their similarities, so it does not need negative samples. Secondly, our model provides a high level of importance for the nearest neighbors, thus avoiding noise information. Thirdly, IRWNRLPI is a model based on an integrated idea, and the integration model gets better results than a single model.Of course, IRWNRLPI also needs to be improved for the following reasons. First of all, the proposed model relies heavily on the known correlation data, but the number of current known lncRNA-protein associations is still very limited. As the number of experimentally validated associations increasing in future, the prediction accuracy of our method will improve. Furthermore, when the training sample changes, the prediction effect will be unstable. In addition, further consideration should be given on how to choose the value of the model parameters more properly.QZ and HL conceived the project, developed the prediction method, designed and implemented the experiments, analyzed the result, and wrote the paper. YZ implemented the experiments, analyzed the result, and wrote the paper. HH, GR, and WZ analyzed the result. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.