Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions

Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the reconstructed similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267.

LncRNA-protein interaction prediction with reweighted feature selection

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias. Furthermore, previous models have not uncovered potential proteins (or lncRNAs) interacting with a new lncRNA (or protein). Finally, the performance of these models can be improved. In this study, we develop a Deep Learning framework with Dual-net Neural architecture to find potential LPIs (LPI-DLDN). First, five LPI datasets are collected. Second, the features of lncRNAs and proteins are extracted by Pyfeat and BioTriangle, respectively. Third, these features are concatenated as a vector after dimension reduction. Finally, a deep learning model with dual-net neural architecture is designed to classify lncRNA-protein pairs. LPI-DLDN is compared with six state-of-the-art LPI prediction methods (LPI-XGBoost, LPI-HeteSim, LPI-NRLMF, PLIPCOM, LPI-CNNCP, and Capsule-LPI) under four cross validations. The results demonstrate the powerful LPI classification performance of LPI-DLDN. Case study analyses show that there may be interactions between RP11-439E19.10 and Q15717, and between RP11-196G18.22 and Q9NUL5. The novelty of LPI-DLDN remains, integrating various biological features, designing a novel deep learning-based LPI identification framework, and selecting the optimal LPI feature subset based on feature importance ranking.

Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.

EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models

lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.

SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information

Non-coding RNAs (ncRNAs) take essential effects on biological processes, like gene regulation. One critical way of ncRNA executing biological functions is interactions between ncRNA and RNA binding proteins (RBPs). Identifying proteins, involving ncRNA-protein interactions, can well understand the function ncRNA. Many high-throughput experiment have been applied to recognize the interactions. As a consequence of these approaches are time- and labor-consuming, currently, a great number of computational methods have been developed to improve and advance the ncRNA-protein interactions research. However, these methods may be not available to all RNAs and proteins, particularly processing new RNAs and proteins. Additionally, most of them cannot process well with long sequence. In this work, a computational method SAWRPI is proposed to make prediction of ncRNA-protein through sequence information. More specifically, the raw features of protein and ncRNA are firstly extracted through the k-mer sparse matrix with SVD reduction and learning nucleic acid symbols by natural language processing with local fusion strategy, respectively. Then, to classify easily, Hilbert Transformation is exploited to transform raw feature data to the new feature space. Finally, stacking ensemble strategy is adopted to learn high-level abstraction features automatically and generate final prediction results. To confirm the robustness and stability, three different datasets containing two kinds of interactions are utilized. In comparison with state-of-the-art methods and other results classifying or feature extracting strategies, SAWRPI achieved high performance on three datasets, containing two kinds of lncRNA-protein interactions. Upon our finding, SAWRPI is a trustworthy, robust, yet simple and can be used as a beneficial supplement to the task of predicting ncRNA-protein interactions.

LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification

BACKGROUND

Long noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experiments are costly and time-consuming. Most computational methods failed to observe the imbalanced characterize of lncRNA-protein interaction (LPI) data. More importantly, they were measured based on a unique dataset, which produced the prediction bias.

RESULTS

In this study, we develop an Ensemble framework (LPI-EnEDT) with Extra tree and Decision Tree classifiers to implement imbalanced LPI data classification. First, five LPI datasets are arranged. Second, lncRNAs and proteins are separately characterized based on Pyfeat and BioTriangle and concatenated as a vector to represent each lncRNA-protein pair. Finally, an ensemble framework with Extra tree and decision tree classifiers is developed to classify unlabeled lncRNA-protein pairs. The comparative experiments demonstrate that LPI-EnEDT outperforms four classical LPI prediction methods (LPI-BLS, LPI-CatBoost, LPI-SKF, and PLIPCOM) under cross validations on lncRNAs, proteins, and LPIs. The average AUC values on the five datasets are 0.8480, 0,7078, and 0.9066 under the three cross validations, respectively. The average AUPRs are 0.8175, 0.7265, and 0.8882, respectively. Case analyses suggest that there are underlying associations between HOTTIP and Q9Y6M1, NRON and Q15717.

CONCLUSIONS

Fusing diverse biological features of lncRNAs and proteins and exploiting an ensemble learning model with Extra tree and decision tree classifiers, this work focus on imbalanced LPI data classification as well as interaction information inference for a new lncRNA (or protein).

LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

Background

Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate.

Results

Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717.

Conclusions

Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04485-x.

NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder

- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification

Background

Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins.

Results

Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637.

Conclusions

Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure

Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.

Recent advances in predicting protein-lncRNA interactions using machine learning methods

Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Background:

MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly.

Objective:

Thus, computational methods are urgently needed to effectively predict miRNA-disease associations.

Methods:

In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model.

Results:

To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively.

Conclusion:

The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.

Recent advances on the machine learning methods in predicting ncRNA-protein interactions

Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that most ncRNAs usually work by interacting with the corresponding RNA-binding proteins. Therefore, ncRNA-protein interaction is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, machine-learning methods for predicting ncRNA-protein interactions are increasingly favored by the researchers. In this review, we summarize several machine learning predictive models of ncRNA-protein interactions over the past few years, and briefly describe the characteristics of these machine learning models. In order to optimize the performance of machine learning models to better predict ncRNA-protein interactions, we give some promising future computational directions at the end.

Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection

Plenty of microbes in our human body play a vital role in the process of cell physiology. In recent years, there is accumulating evidence indicating that microbes are closely related to many complex human diseases. In-depth investigation of disease-associated microbes can contribute to understanding the pathogenesis of diseases and thus provide novel strategies for the treatment, diagnosis, and prevention of diseases. To date, many computational models have been proposed for predicting microbe-disease associations using available similarity networks. However, these similarity networks are not effectively fused. In this study, we proposed a novel computational model based on multi-data integration and network consistency projection for Human Microbe-Disease Associations Prediction (HMDA-Pred), which fuses multiple similarity networks by a linear network fusion method. HMDA-Pred yielded AUC values of 0.9589 and 0.9361 ± 0.0037 in the experiments of leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, in case studies, 10, 8, and 10 out of the top 10 predicted microbes of asthma, colon cancer, and inflammatory bowel disease were confirmed by the literatures, respectively.

PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction

Protein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can’t be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein–RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein–RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it’s freely available.

Xie et al. report a new computational method PRIME-3D2D to predict binding sites of protein–RNA interaction by considering protein 3D structure and RNA 2D structure. It is freely available, performs better than other binding sites predictor and is as good as PRIME to model protein–RNA complex.

LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick

Long noncoding RNAs (lncRNAs) play critical roles in many pathological and biological processes, such as post-transcription, cell differentiation and gene regulation. Increasingly more studies have shown that lncRNAs function through mainly interactions with specific RNA binding proteins (RBPs). However, experimental identification of potential lncRNA-protein interactions is costly and time-consuming. In this work, we propose a novel convolutional neural network-based method with the copy-padding trick (named LPI-CNNCP) to predict lncRNA-protein interactions. The copy-padding trick of the LPI-CNNCP convert the protein/RNA sequences with variable-length into the fixed-length sequences, thus enabling the construction of the CNN model. A high-order one-hot encoding is also applied to transform the protein/RNA sequences into image-like inputs for capturing the dependencies among amino acids (or nucleotides). In the end, these encoded protein/RNA sequences are feed into a CNN to predict the lncRNA-protein interactions. Compared with other state-of-the-art methods in 10-fold cross-validation (10CV) test, LPI-CNNCP shows the best performance. Results in the independent test demonstrate that our LPI-CNNCP can effectively predict the potential lncRNA-protein interactions. We also compared the copy-padding trick with two other existing tricks (i.e., zero-padding and cropping), and the results show that our copy-padding rick outperforms the zero-padding and cropping tricks on predicting lncRNA-protein interactions. The source code of LPI-CNNCP and the datasets used in this work are available at https://github.com/NWPU-903PR/LPI-CNNCP for academic users.

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

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

Predicting lncRNA-Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA–protein interactions are usually involved. Therefore, identifying lncRNA–protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA–protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA–protein interaction predictors usually require prior knowledge of lncRNA–protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA–protein interactions. In this paper, we explored a novel way to predict lncRNA–protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA–protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA–protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).

Heterogeneous graph inference based on similarity network fusion for predicting lncRNA–miRNA interaction

LncRNA and miRNA are two non-coding RNA types that are popular in current research. LncRNA interacts with miRNA to regulate gene transcription, further affecting human health and disease. Accurate identification of lncRNA–miRNA interactions contributes to the in-depth study of the biological functions and mechanisms of non-coding RNA. However, relying on biological experiments to obtain interaction information is time-consuming and expensive. Considering the rapid accumulation of gene information and the few computational methods, it is urgent to supplement the effective computational models to predict lncRNA–miRNA interactions. In this work, we propose a heterogeneous graph inference method based on similarity network fusion (SNFHGILMI) to predict potential lncRNA–miRNA interactions. First, we calculated multiple similarity data, including lncRNA sequence similarity, miRNA sequence similarity, lncRNA Gaussian nuclear similarity, and miRNA Gaussian nuclear similarity. Second, the similarity network fusion method was employed to integrate the data and get the similarity network of lncRNA and miRNA. Then, we constructed a bipartite network by combining the known interaction network and similarity network of lncRNA and miRNA. Finally, the heterogeneous graph inference method was introduced to construct a prediction model. On the real dataset, the model SNFHGILMI achieved AUC of 0.9501 and 0.9426 ± 0.0035 based on LOOCV and 5-fold cross validation, respectively. Furthermore, case studies also demonstrate that SNFHGILMI is a high-performance prediction method that can accurately predict new lncRNA–miRNA interactions. The Matlab code and readme file of SNFHGILMI can be downloaded from https://github.com/cj-DaSE/SNFHGILMI.

LncRNA and miRNA are two non-coding RNA types that are popular in current research.

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination

BACKGROUND

Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods.

RESULTS

In this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs.

CONCLUSION

The study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction.

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction

Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.

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

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

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

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

Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

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

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/.

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/.

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

BACKGROUND

Effectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.

RESULTS

In this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained. We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.

CONCLUSION

CPredictor3.0 can serve as a promising tool of protein complex prediction.