LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy

Xi, Wen-Yu; Zhou, Feng; Gao, Ying-Lian; Liu, Jin-Xing; Zheng, Chun-Hou

doi:10.1109/tcbb.2022.3215194

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…WGRCMF: 39 Prediction algorithm based on collaborative matrix factorization and graph regularization. LDCMFC: 23 Matrix factorization method based on correlation entropy. SCCPMD: 22 Algorithm based on probabilistic matrix factorization and logistic function-corrected similarity.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, they proposed the probability matrix factorization method, SCCPMD, preprocessing the similarity matrix using a logistic function, and then calculating the final score matrix based on probability matrix factorization methods. Xi et al 23 believed that traditional Euclidean calculation methods have poor robustness when predicting noisy data, thus introducing a collaborative matrix factorization method based on mutual information entropy (LDCMFC). Although current matrix factorization methods have achieved promising results in predicting the association between LncRNAs and diseases, they have largely overlooked the cold start problem caused by the extreme sparsity of the LncRNA-disease association matrix.…”

Section: ■ Introductionmentioning

confidence: 99%

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HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Xie,

Li,

Lin

et al. 2024

J. Chem. Inf. Model.

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Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging highorder perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Xie,

Li,

Lin

et al. 2024

J. Chem. Inf. Model.

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“…Machine learning methods have been applied to various association discovery tasks (Zou et al, 2018;Peng et al, 2019;Shen et al, 2022;Wu et al, 2022;Yu et al, 2022;Lin et al, 2023a;Peng et al, 2023a;Peng et al, 2023b;Peng et al, 2024b;Han et al, 2023;Liu and Zhang, 2023;Qi and Zou, 2023;Xiong et al, 2023;Xu et al, 2024;Zhang et al, 2024). Consequently, machine learning algorithms have been broadly applied in LDA prediction, for example, collaborative filtering (Yu et al, 2019), graph regularization (Liu et al, 2020;, matrix factorization (Fu et al, 2018;Wang et al, 2020;Xi et al, 2022), heterogeneous graph learning framework, (Cao et al, 2023), and ensemble learning models (Peng et al, 2022a). Notably, deep learning has been broadly applied due to its powerful classification performance (Sun et al, 2022;Wang et al, 2023b;Hu et al, 2023;Jiang et al, 2023;Zhou et al, 2024a), such as in the graph convolution network (Wang W. et al, 2022), node2vec (Li et al, 2021), collaborative deep learning (Lan et al, 2020), deep neural network (Wei et al, 2020), deep multi-network embedding (Ma, 2022), graph autoencoder (Liang et al, 2023;Zhou et al, 2024b), and a capsule network with the attention mechanism .…”

Section: Introductionmentioning

confidence: 99%

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Zhou,

Peng,

Zeng

et al. 2024

Front. Genet.

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Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

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“…In addition, several network-based methods have been developed to identify potential LDAs. These methods include multi-layer network model (MHRWR) [ 42 ], Laplace normalized random walk with restart (LRWRHLDA) [ 43 ], weighted graph regularized collaborative matrix factorization (WGRCMF) [ 44 ], collaborative matrix factorization with the maximized correntropy (LDCMFC) [ 45 ], dual sparse collaborative matrix factorization (WGRCMF) [ 44 ] and graph regularized nonnegative matrix factorization (LDGRNMF) [ 46 ]. Based on existing studies, Chen et al.…”

Section: Introductionmentioning

confidence: 99%

LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

Peng,

Huang,

et al. 2023

Briefings in Bioinformatics

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Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA–disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA–disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

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LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy

Cited by 7 publications

References 44 publications

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

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