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

Peng, Lihong; Tan, Jingwei; Tian, Xiongfei; Zhou, Liqian

doi:10.1007/s12539-021-00483-y

Cited by 31 publications

(28 citation statements)

References 71 publications

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“…Ensemble learning integrates multiple results from individual models and demonstrates better performance compared to individual models ( Zhou et al, 2021a ; Peng et al, 2022b ). Therefore, in this study, we develop an ensemble learning model by combining random walk with restart and Laplacian regularized least squares to improve the CDA’s prediction performance by Eq.…”

Section: Methodsmentioning

confidence: 99%

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Tan

Liu

et al. 2022

Front. Genet.

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Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.

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Section: Methodsmentioning

confidence: 99%

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Tan

Liu

et al. 2022

Front. Genet.

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“…Therefore, lncRNAs can be used as potential biomarkers and therapeutic targets in cancers by interacting with proteins (Chandra Gupta and Nandan Tripathi, 2017). For example, Peng et al and her groups (Peng et al, 2021a;Zhou L. Q. et al, 2021;Peng et al, 2021b;Zhou L. et al, 2021;Tian et al, 2021;Peng et al, 2022) designed a series of state-of-the-art lncRNA-protein interaction prediction methods and significantly improved biomarker identification for various diseases. In addition, lncRNA SNHG14, BCRT1, DSCAM-AS1, MaTAR24, and HOTAIR have been validated to densely link to breast cancer (Niknafs et al, 2016;Dong et al, 2018;Chang et al, 2020;Liang et al, 2020;Yang et al, 2022;Xue et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk

Guo

Yan²,

Kong³

et al. 2022

Front. Genet.

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Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.

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“…Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs involved in diverse biological processes Peng et al (2022) ; Jia and Luan (2022) . lncRNAs are aberrantly expressed in numerous cancers and demonstrate crucial roles in oncogenic and tumor suppressive activities Zhou et al (2021) ; Liu et al (2022) .…”

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confidence: 99%

Editorial: Machine Learning-Based Methods for RNA Data Analysis

et al. 2022

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

Cited by 31 publications

References 71 publications

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk

Editorial: Machine Learning-Based Methods for RNA Data Analysis

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