ArticleiScience2021
RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- A novel NLP-based method and algorithm to discover RNA-binding protein (RBP) motifs, contexts, binding preferences, and interactions.RNA (New York, N.Y.) · 2026Article
- PTF-Vāc: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant transcription factor binding sites.Plant communications · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- Emerging RNA-centric technologies to probe RNA-protein interactions: importance in decoding the life cycle of positive sense single strand RNA viruses and antiviral discovery.Frontiers in cellular and infection microbiology · 2025Review
- RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.Computational and structural biotechnology journal · 2024Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
- A systematic benchmark of machine learning methods for protein-RNA interaction prediction.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.
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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.