ArticleNature communications2018
PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Context-aware sequence-to-function model of human gene regulation.Nature communications · 2026Article
- Unified imputation of missing data modalities and features in multi-omic data via shared representation learning.bioRxiv : the preprint server for biology · 2026Article
- GECSI: Large-scale chromatin state imputation from gene expression.bioRxiv : the preprint server for biology · 2025Article
- Modelling transcription with explainable AI uncovers context-specific epigenetic gene regulation at promoters and gene bodies.PLoS genetics · 2025Article
- Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.PLoS computational biology · 2025Article
- CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.Genome biology · 2025Article
- Predicting chromatin conformation contact maps.PloS one · 2025Article
- Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Nature communications · 2024Article
- dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.Briefings in bioinformatics · 2024Article
- The structure is the message: Preserving experimental context through tensor decomposition.Cell systems · 2024Review
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Cancer epigenetics: from laboratory studies and clinical trials to precision medicine.Cell death discovery · 2024Review
- The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.Genome biology · 2023Article
- Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project.BMC medical research methodology · 2022Article
- Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome research · 2022Article
- Continuous chromatin state feature annotation of the human epigenome.Bioinformatics (Oxford, England) · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Prediction of histone post-translational modification patterns based on nascent transcription data.Nature genetics · 2022Article
- Asymmetric Predictive Relationships Across Histone Modifications.Nature machine intelligence · 2022Article
- Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.PloS one · 2022Article
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5 authors.
Funding
Abstract
The Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenomics Project seek to characterize the epigenome in diverse cell types using assays that identify, for example, genomic regions with modified histones or accessible chromatin. These efforts have produced thousands of datasets but cannot possibly measure each epigenomic factor in all cell types. To address this, we present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to computationally impute missing experiments. PREDICTD leverages an elegant model called "tensor decomposition" to impute many experiments simultaneously. Compared with the current state-of-the-art method, ChromImpute, PREDICTD produces lower overall mean squared error, and combining the two methods yields further improvement. We show that PREDICTD data captures enhancer activity at noncoding human accelerated regions. PREDICTD provides reference imputed data and open-source software for investigating new cell types, and demonstrates the utility of tensor decomposition and cloud computing, both promising technologies for bioinformatics.
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