ArticleScientific reports2021
A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Computational understanding of non-coding RNA pairwise interactions.Frontiers in artificial intelligence · 2026Article
- A Comprehensive Pipeline for Long Non-Coding RNA Discovery and Characterization in Cancer.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.Molecular diversity · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction prediction in heterogeneous graphs.Briefings in bioinformatics · 2025Article
- Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.Molecular therapy. Nucleic acids · 2024Article
- LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.Interdisciplinary sciences, computational life sciences · 2024Article
- Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion.BMC genomics · 2023Article
- lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.Database : the journal of biological databases and curation · 2023Article
- A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining.Frontiers in genetics · 2023Article
- Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.BMC bioinformatics · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk.Frontiers in genetics · 2022Article
- Editorial: Machine Learning-Based Methods for RNA Data Analysis.Frontiers in genetics · 2022Article
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5 authors.
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Abstract
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.
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