ArticleBioinformatics (Oxford, England)2016
Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
- A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.Scientific reports · 2025Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- Transcriptomics, Proteomics and Bioinformatics in Atrial Fibrillation: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.Bioinformatics (Oxford, England) · 2024Article
- DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures.Computational and structural biotechnology journal · 2024Article
- Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.PLoS computational biology · 2024Article
- Review
- Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments.iScience · 2024Article
- KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.Journal of translational medicine · 2023Article
- Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.Biology · 2023Article
- A systematic benchmark of machine learning methods for protein-RNA interaction prediction.Briefings in bioinformatics · 2023Article
- CRIT: Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization.Molecular therapy. Nucleic acids · 2022Article
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
- Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP.Genome biology · 2022Article
- DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.BMC bioinformatics · 2022Article
- PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information.Bioinformatics (Oxford, England) · 2022Article
- Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data.BMC bioinformatics · 2022Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
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5 authors.
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Abstract
motivationRNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein-RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein-RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed.
resultsWe have developed an integrative orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources and discover non-overlapping, class-specific RNA binding patterns of varying strengths. The orthogonality constraint halves the effective size of the factor model and outperforms other NMF models in predicting RBP interaction sites on RNA. We have integrated the largest data compendium to date, which includes 31 CLIP experiments on 19 RBPs involved in splicing (such as hnRNPs, U2AF2, ELAVL1, TDP-43 and FUS) and processing of 3'UTR (Ago, IGF2BP). We show that the integration of multiple data sources improves the predictive accuracy of retrieval of RNA binding sites. In our study the key predictive factors of protein-RNA interactions were the position of RNA structure and sequence motifs, RBP co-binding and gene region type. We report on a number of protein-specific patterns, many of which are consistent with experimentally determined properties of RBPs. AVAILABILITY AND IMPLEMENTATION: The iONMF implementation and example datasets are available at https://github.com/mstrazar/ionmf CONTACT: : tomaz.curk@fri.uni-lj.si SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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