ArticlePeerJ2025
MDDeep-Ace: species-specific acetylation site prediction based on multi-domain adaptation.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Beyond the Capsid: How Can Post-Translational Modifications Modulate the Multifunctionality of the Orthoflavivirus Capsid Protein?Molecules (Basel, Switzerland) · 2026Review
- PTM encoding: decoding the mechanisms of exercise-induced metabolic memory through spatiotemporal modification networks.Frontiers in sports and active living · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lysine post-translational modification (PTM) is pivotal in regulating diverse cellular processes, profoundly impacting protein structure and function. Over recent decades, numerous experimental techniques have advanced PTM site identification, significantly contributing to research progress. However, these methods are time-intensive and labor-intensive. Deep learning technologies have shown promise in predicting PTM sites, yet current approaches struggle with species-specific PTM site prediction. Methods: We introduce MDDeep-Ace, a novel deep learning method based on multi-domain adaptation for predicting lysine acetylation sites. By integrating data from multiple species, MDDeep-Ace enhances the generalization of species-specific prediction models, improving predictive performance. Results: Experimental findings illustrate that our proposed multi-domain adaptation approach significantly enhances prediction accuracy across multiple species, surpassing existing lysine acetylation site prediction tools.
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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.