ArticlePatterns (New York, N.Y.)2022
DAISM-DNN
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
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Who cites it
16 citing papers in PubMed.
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data.Briefings in bioinformatics · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Community assessment of methods to deconvolve cellular composition from bulk gene expression.Nature communications · 2024Article
- Adaptive digital tissue deconvolution.Bioinformatics (Oxford, England) · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- BuDDI:bioRxiv : the preprint server for biology · 2024Article
- Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNNSTAR protocols · 2022Article
- Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma.Communications medicine · 2022Article
- Editorial: Integrative analysis of single-cell and/or bulk multi-omics sequencing data.Frontiers in genetics · 2022Article
- Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNN
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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.