ArticleGenome biology2025
A message passing framework for precise cell state identification with scClassify2.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.Molecular diversity · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Cell annotation is crucial for downstream exploration. Although many approaches, spanning from classic statistics to large language models, have been developed, most of their focus is on distinct cell types and overlook sequential cell populations. Here, we propose an annotation method, scClassify2, to specifically focus on adjacent cell state identification. By incorporating prior biological knowledge through a novel dual-layer architecture and ordinal regression, scClassify2 achieves competitive performance compared to other state-of-the-art methods. Besides single-cell RNA-sequencing data, scClassify2 is generalizable from different platforms including subcellular spatial transcriptomics data. We also develop a web server for academic uses.
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.