scholarly journals Prediction of lncRNA–Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

2022 ◽  
Vol 12 ◽  
Author(s):  
Jianwei Li ◽  
Mengfan Kong ◽  
Duanyang Wang ◽  
Zhenwu Yang ◽  
Xiaoke Hao

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jianwei Li ◽  
Jianing Li ◽  
Mengfan Kong ◽  
Duanyang Wang ◽  
Kun Fu ◽  
...  

Abstract Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. Results In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. Conclusions We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dayun Liu ◽  
Yibiao Huang ◽  
Wenjuan Nie ◽  
Jiaxuan Zhang ◽  
Lei Deng

Abstract Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.


2021 ◽  
Vol 7 (2) ◽  
pp. 30
Author(s):  
Laeya Baldini ◽  
Bruno Charpentier ◽  
Stéphane Labialle

Box C/D small nucleolar RNAs (C/D snoRNAs) represent an ancient family of small non-coding RNAs that are classically viewed as housekeeping guides for the 2′-O-methylation of ribosomal RNA in Archaea and Eukaryotes. However, an extensive set of studies now argues that they are involved in mechanisms that go well beyond this function. Here, we present these pieces of evidence in light of the current comprehension of the molecular mechanisms that control C/D snoRNA expression and function. From this inventory emerges that an accurate description of these activities at a molecular level is required to let the snoRNA field enter in a second age of maturity.


2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Ravi V Shah ◽  
Olivia Ziegler ◽  
Kahraman Tanriverdi ◽  
Jian Rong ◽  
Martin Larson ◽  
...  

While increased left ventricular mass (LVM) is strongly associated with incident heart failure (HF), events during transition from increased LVM to HF remain unclear. Extracellular non-coding RNAs (ex-RNAs) have been implicated in cardiac hypertrophy, though whether these ex-RNAs reflect important pathways in HF in humans is underexplored. In >2,000 individuals with concomitant M-mode echocardiography and ex-RNA measurements in the Framingham Heart Study, we found that lower circulating concentrations of three ex-RNAs—miR-20a-5p, miR-106b-5p, miR-17-5p—were associated with (1) greater LVM (+ one other pre-clinical phenotype, e.g., left atrial dimension or LVEDV) and (2) greater incident HF risk over a median follow-up 7.7 years ( Fig. A ). These 3 miRNAs were members of a tight cluster, regulating 883 mRNAs in common, associated with “hypertension” (OMIM) and biological process relevant to HF, including TGF-β signaling. We observed an increase in myocardial expression of these miRNAs during different phases of hypertrophy/HF development ( Fig. C, D ). Using gain and loss of function in vitro , our preliminary results suggest up-regulation of cardiomyocyte miR-106b expression abrogates expression of pathologic hypertrophy markers (ANP and BNP) during phenylephrine treatment, consistent with in silico results suggesting broad connections between miR-106b targets and natriuretic peptide signaling ( Fig. B, E-F ). These results provide translational evidence that circulating miRNAs associated with hypertrophy in patients may be protective in the transition from hypertrophy to HF at the molecular level.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wang ◽  
Jing Zhang

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.


Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1012 ◽  
Author(s):  
Xuan ◽  
Pan ◽  
Zhang ◽  
Liu ◽  
Sun

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.


Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 608 ◽  
Author(s):  
Yan Li ◽  
Junyi Li ◽  
Naizheng Bian

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.


Author(s):  
Hongying Zhao ◽  
Jian Shi ◽  
Yunpeng Zhang ◽  
Aimin Xie ◽  
Lei Yu ◽  
...  

Abstract Long non-coding RNAs (lncRNAs) are associated with human diseases. Although lncRNA–disease associations have received significant attention, no online repository is available to collect lncRNA-mediated regulatory mechanisms, key downstream targets, and important biological functions driven by disease-related lncRNAs in human diseases. We thus developed LncTarD (http://biocc.hrbmu.edu.cn/LncTarD/ or http://bio-bigdata.hrbmu.edu.cn/LncTarD), a manually-curated database that provides a comprehensive resource of key lncRNA–target regulations, lncRNA-influenced functions, and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD offers (i) 2822 key lncRNA–target regulations involving 475 lncRNAs and 1039 targets associated with 177 human diseases; (ii) 1613 experimentally-supported functional regulations and 1209 expression associations in human diseases; (iii) important biological functions driven by disease-related lncRNAs in human diseases; (iv) lncRNA–target regulations responsible for drug resistance or sensitivity in human diseases and (v) lncRNA microarray, lncRNA sequence data and transcriptome data of an 11 373 pan-cancer patient cohort from TCGA to help characterize the functional dynamics of these lncRNA–target regulations. LncTarD also provides a user-friendly interface to conveniently browse, search, and download data. LncTarD will be a useful resource platform for the further understanding of functions and molecular mechanisms of lncRNA deregulation in human disease, which will help to identify novel and sensitive biomarkers and therapeutic targets.


Author(s):  
Ryosuke Furuta ◽  
Naoto Inoue ◽  
Toshihiko Yamasaki

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied.We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.


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