ArticleFrontiers in genetics2021
Deep Learning Enables Fast and Accurate Imputation of Gene Expression.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications.BMC medical research methodology · 2024Article
- Deep Learning Methods for Omics Data Imputation.Biology · 2023Review
- The impact of imputation quality on machine learning classifiers for datasets with missing values.Communications medicine · 2023Article
- Hypergraph factorization for multi-tissue gene expression imputation.Nature machine intelligence · 2023Article
- MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.Bioinformatics (Oxford, England) · 2023Article
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- Bioinformatic analysis of the molecular mechanisms underlying the progression of bone defects.Frontiers in medicine · 2023Article
- Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors.Frontiers in oncology · 2022Article
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4 authors.
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Abstract
A question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we propose two novel deep learning methods, PMI and GAIN-GTEx, for gene expression imputation. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We show that our approaches compare favorably to several standard and state-of-the-art imputation methods in terms of predictive performance and runtime in two case studies and two imputation scenarios. In comparison conducted on the protein-coding genes, PMI attains the highest performance in inductive imputation whereas GAIN-GTEx outperforms the other methods in in-place imputation. Furthermore, our results indicate strong generalization on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types.
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