ArticlePatterns (New York, N.Y.)2025
Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.International journal of pharmaceutics: X · 2026Review
- Article
- Ashwin and FAM98 paralogs define nuclear and cytoplasmic RNA ligase complexes for tRNA biogenesis.Nature communications · 2026Article
- N-Terminal Basic Helix and S120 Phosphorylation Cooperatively Regulate Nuclear Localization of UBE2A/B in Mechanotransduction.Biology · 2026Article
- Establishment of an Indirect ELISA Detection Method for Porcine Circovirus 3 Based on Soluble Cap Protein.Veterinary sciences · 2026Article
- Dissecting the propensity of RIM1 subdomains to form phase condensates.Scientific reports · 2026Article
- Cytoplasmic localization of pseudouridine synthase 7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance.Nature communications · 2026Article
- Nuclear-Cytoplasmic Axis in Cancer: From Protein Mislocalization to Anticancer Drug Resistance.Oncology research · 2026Review
- Studies of the motifs that regulate subcellular localization reveal differences between the homologous neuronal proteins NeuroD1 and NeuroD2.Cell communication and signaling : CCS · 2025Article
- Cytoplasmic localization of PUS7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance.bioRxiv : the preprint server for biology · 2025Article
- Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units.Patterns (New York, N.Y.) · 2025Article
- C2H2 Zinc Finger Protein Family Analysis ofPlants (Basel, Switzerland) · 2024Article
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3 authors.
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Abstract
We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs.
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