ArticleNature machine intelligence2022
Asymmetric Predictive Relationships Across Histone Modifications.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Multi-omics based and AI-driven drug repositioning for epigenetic therapy in female malignancies.Journal of translational medicine · 2025Review
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
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Authors and funding
2 authors.
Funding
Abstract
Decoding the epigenomic landscapes in diverse tissues and cell types is fundamental to understanding molecular mechanisms underlying many essential cellular processes and human diseases. Recent advances in artificial intelligence provide new methods and strategies for imputing unknown epigenomes based on existing data, yet how to reveal the predictive relationships among epigenetic marks remains largely unexplored. Here we present a machine learning approach for epigenomic imputation and interpretation. Through dissection of the spatial contributions from six histone marks, we reveal the prevalent and asymmetric cross-prediction relationships among these marks. Meanwhile, our approach achieved high predictive performance on held-out prospective epigenomes and outperformed the state-of-the-art. To facilitate future research, we further applied this approach to impute a total of 527 and 2,455 unavailable genome-wide histone modification signal tracks for the ENCODE3 and Roadmap datasets, respectively.
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Registered trials
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