ArticleNature biomedical engineering2026
A self-adaptive and versatile tool for eliminating multiple undesirable variations from large-scale transcriptomes.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Ensuring multiomics data reproducibility for artificial intelligence with reference materials as a common calibrator.Nature biotechnology · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- A self-adaptive and versatile tool for eliminating multiple undesirable variations from large-scale transcriptomes.Nature biomedical engineering · 2026Article
- Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of true biological signals from diverse undesirable variations in large-scale transcriptomes is essential for downstream discoveries. Here we develop a universal deep neural network, called DeepAdapter, to eliminate various undesirable variations including batch, platform, purity and other unknown sources from transcriptomic data. Our approach automatically learns the corresponding denoising strategies to adapt to different situations. The data-driven strategies are flexible and highly attuned to the transcriptomic data that requires denoising, yielding reduced undesirable variation originating from batches, sequencing platforms and biosamples with varied purity beyond manually designed schemes. Comprehensive evaluations across multiple batches, different RNA measurement technologies and heterogeneous biosamples demonstrate that DeepAdapter can robustly correct diverse undesirable variations and accurately preserve biological signals, the faithful gene expression patterns that facilitate reliable biomarker discovery, transcriptomic network analysis and comprehensive biological characterization. Our findings indicate that DeepAdapter can act as a versatile tool for the comprehensive denoising of the large and heterogeneous transcriptome across a wide variety of application scenarios.
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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 Socratic graph.