ReviewFrontiers in genetics2019
Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 41 citations in OpenAlex.
- LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.Briefings in bioinformatics · 2025Article
- Computational Prediction of Gene Regulation by lncRNAs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- Pivotal functions and impact of long con-coding RNAs on cellular processes and genome integrity.Journal of biomedical science · 2024Review
- The Idiopathic Pulmonary Fibrosis-Associated Single Nucleotide Polymorphism RS35705950 Is Transcribed in a MUC5B Promoter Associated Long Non-Coding RNA (AC061979.1).Non-coding RNA · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- Editorial: Machine learning-based methods for RNA data analysis-Volume II.Frontiers in genetics · 2022Article
- Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk.Frontiers in genetics · 2022Article
- A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data.Frontiers in oncology · 2022Article
- LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification.BioData mining · 2021Article
- LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification.BMC bioinformatics · 2021Article
- Article
- Review
- LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification.BMC bioinformatics · 2021Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- Decoding LncRNAs.Cancers · 2021Review
- Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.Frontiers in genetics · 2021Article
- Epigenetic Regulation of the Vascular Endothelium by Angiogenic LncRNAs.Frontiers in genetics · 2021Review
- Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization.Frontiers in genetics · 2021Article
- The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis.Non-coding RNA · 2020Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.