Experiments

Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).

• Embeddings from language models are good learners for single-cell data analysis.

  PMID 41726097 · PMC12921509 · Patterns (New York, N.Y.) · 2026 · 8 claims · 8 setups

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  scELMo combines LLM-derived embeddings of gene and cell metadata with raw single-cell expression data via matrix operations to generate cell embeddings without pretraining a new model

  scrna-seq multi-batch diseased perturbation clustering batch cell-type

• Exploring phenotype-related single-cells through attention-enhanced representation learning.

  PMID 41566378 · PMC12906058 · Genome medicine · 2026 · 6 claims · 5 setups

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  scPhase, an attention-based multiple instance learning (AMIL) framework with Mixture-of-Experts aggregation, predicts sample-level clinical phenotypes from raw scRNA-seq data and generalizes across patient cohorts

  pbmcs brain tumor scrna-seq

• Prediction of myeloid malignant cells in Fanconi anemia using machine learning.

  PMID 41557613 · PMC12818649 · PloS one · 2026 · 6 claims · 7 setups

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  A DNN classifier trained on AML scRNA-seq data accurately predicts AML-like transcriptional profiles at single-cell resolution

  bone predicted-aml healthy scrna-seq reference-based stemnet

• ICE: robust detection of cellular senescence from weak single-cell signatures using imputation-based marker refinement.

  PMID 41668152 · PMC12990438 · Genome biology · 2026 · 8 claims · 7 setups

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  Senescence-associated marker genes show weak, non-specific expression across human tissues and cell types compared to canonical tissue/cell-type markers

  tissues pancreas pancreatic brain bulk scrna-seq train

• Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage.

  PMID 41779826 · PMC12959883 · eLife · 2026 · 8 claims · 5 setups

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  DeepTX is an interpretable, scalable deep learning inference framework that links mechanistic transcription models to scRNA-seq data to infer genome-wide transcriptional burst kinetics

  synthetic embryonic colorectal computational stochastic scrna-seq deep