lncRNA-disease association prediction based on the weight matrix and projection score

Wang, Bo; Zhang, Chao; Du, Xiaohong; Zheng, Xiaodong; Li, Jing-You

doi:10.1371/journal.pone.0278817

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…Therefore, we decided to begin the model improvement with one-hot-BiGRU. To guarantee a fair comparison, we adjusted the number of hidden units in [20,30,40] for all the models to select the best parameter. The tuned number of hidden units for each model is shown in Table 1.…”

Section: Comparison Of Traditional Deep Learning Methods and Coding M...mentioning

confidence: 99%

“…Table 1. Performance of different deep learning models with different coding methods (the Time column indicates the model training time, and the Hidden column denotes the optimal number of neurons in the hidden layer for various models, with the range adjusted from [20,30,40]). To further improve the performance of the one-hot BiGRU model, we inserted CNNs before BiGRU to initially extract the local contextual information [41] and spatial information [42,43] of the sequences.…”

Section: The Effect Of Hyperparameters On Cnn-bigru's Performancementioning

confidence: 99%

“…LncRNAs were initially considered to be transcriptional noise with no biological function, which were transcribed by RNA polymerase II [17]. Recent studies have shown the close association of lncRNAs with various diseases, and a large number of computational methods have been developed as a result [18][19][20][21]. LncRNAs are equally vital in plants, playing significant roles in various biological processes.…”

Section: Introductionmentioning

confidence: 99%

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CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Nie

Huang

et al. 2023

Plants

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Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 − 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.

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Section: Comparison Of Traditional Deep Learning Methods and Coding M...mentioning

confidence: 99%

Section: The Effect Of Hyperparameters On Cnn-bigru's Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Nie

Huang

et al. 2023

Plants

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“…Then, six heterogeneous networks involving known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations are built to design the multi-layer network. In [ 29 ] the LDAP-WMPS LDA prediction model is proposed, based on weight matrix and projection score. LDAP-WMPS consists on three steps: the first one computes the disease projection score; the second step calculates the lncRNA projection score; the third step fuses the disease projection score and the lncRNA projection score proportionally, then it normalizes them to get the prediction score matrix.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Bonomo,

Rombo

2024

BMC Bioinformatics

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Motivation Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. Results We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. Availability Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

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Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism

Wang,

Qiao,

Feng

2024

Sci Rep

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LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A.

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lncRNA-disease association prediction based on the weight matrix and projection score

Cited by 4 publications

References 60 publications

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism

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