ArticlePatterns (New York, N.Y.)2026
Deep learning enabled prediction of nuclear lamina-associated chromatin.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Deep learning enabled prediction of nuclear lamina-associated chromatin.Patterns (New York, N.Y.) · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
Tethering of chromatin regions to the nuclear lamina contributes to genome organization and gene regulation. Though several molecular and epigenomic determinants of lamina association have been studied, the influence of genomic sequences has received less attention. Here, we present lamina-associated domain finder (LADDER), a multimodal deep learning model to predict lamina association based on DNA sequence, gene density, and long interspersed nuclear element (LINE)1 and short interspersed nuclear element (SINE) densities. The predictions made by LADDER are cell-type independent. Using LADDER's predictive ability and performing
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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.