Prediction of lncRNA–disease associations based on inductive matrix completion

Lu, Chengqian; Yang, Mingzhu; Luo, Feng; Wu, Fang‐Xiang; Li, Min; Pan, Yi; Li, Yaohang; Wang, Jianxin

doi:10.1093/bioinformatics/bty327

Cited by 239 publications

(152 citation statements)

References 41 publications

(43 reference statements)

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“…In future, we would combine the EMRs with the MRI images to analyze the OA disease. We would also try to predict drug indications by integrating related data sources and validated information of drugs and diseases using matrix completion method [34, 35]. …”

Section: Discussionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

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BackgroundOsteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.ResultsIn this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.ConclusionCompared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

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Section: Discussionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

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“…In addition, other integration methods of receptor similarity also should be considered in the future. Finally, other latest matrix factorization methods also should be considered, such as DNRLMF-MDA [37], DRRS [38], SIMCLDA [39] and BNNR [40]. Therefore, we would like to develop a more effective method for predicting virus-receptor interactions by addressing the above limitations in the future.…”

Section: Resultsmentioning

confidence: 99%

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Cheng

Duan

et al. 2019

BMC Bioinformatics

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BackgroundViral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited.ResultIn this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF).ConlusionThe experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

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“…In this section, we compared the proposed method with three stateof-the-art methods i.e. BiwalkLDA (Hu et al, 2019), SIMCLDA (Lu et al, 2018) and KATZLDA (Chen, 2015) on the aforementioned three datasets. Firstly, two evaluation metrics Leave-One-Out Cross Valuation (LOOCV) and five-fold Cross Validation (CV) were conducted to systematically evaluate the prediction performance of each method.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Fu et al decomposed the data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore the intrinsic as well as the shared structure, and then used the optimized low-rank matrices to obtain the potential associations (Fu et al, 2018). Lu et al extracted a set of primary feature vectors and used the inductive matrix completion framework to infer the lncRNA-disease association (Lu et al, 2018). Lan et al constructed a web server for lncRNA-disease association prediction by integrating multiple biological data resources (Lan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

Tan

Sun

et al. 2020

Front. Genet.

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Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules longer than 200 nucleotides. Recent studies have uncovered their functional roles in diverse cellular processes and tumorigenesis. Therefore, identifying novel disease-related lncRNAs might deepen our understanding of disease etiology. However, due to the relatively small number of verified associations between lncRNAs and diseases, it remains a challenging task to reliably and effectively predict the associated lncRNAs for given diseases. In this paper, we propose a novel multiview consensus graph learning method to infer potential disease-related lncRNAs. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases by taking advantage of the known associations. We then iteratively learn a consensus graph from the multiple input matrices and simultaneously optimize the predicted association probability based on a multi-label learning framework. To convey the utility of our method, three state-of-the-art methods are compared with our method on three widely used datasets. The experiment results illustrate that our method could obtain the best prediction performance under different cross validation schemes. The case study analysis implemented for uterine cervical neoplasms further confirmed the utility of our method in identifying lncRNAs as potential prognostic biomarkers in practice.

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Prediction of lncRNA–disease associations based on inductive matrix completion

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 239 publications

References 41 publications

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

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