ArticleCommunications biology2021
Deep representation features from DreamDIA
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Data-independent acquisition-based quantitative proteomics in plants.Frontiers in plant science · 2026Review
- Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.Communications chemistry · 2025Article
- DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis.Nature communications · 2025Article
- IRGL-RRI: interpretable graph representation learning for plant RNA-RNA interaction discovery.Frontiers in plant science · 2025Article
- Disc-Hub: a python package for benchmarking machine learning strategies in DIA-MS identification.Bioinformatics advances · 2025Article
- Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics.ACS omega · 2024Article
- SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics.Interdisciplinary sciences, computational life sciences · 2024Article
- Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics.Molecular & cellular proteomics : MCP · 2024Review
- The mouse multi-organ proteome from infancy to adulthood.Nature communications · 2024Article
- Dear-DIAResearch (Washington, D.C.) · 2023Article
- Measuring depression severity based on facial expression and body movement using deep convolutional neural network.Frontiers in psychiatry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed DreamDIA
Indexed as
Identifiers
What OpenQuestion holds
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.