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).

• Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.

  PMID 42045691 · PMC13175893 · Nature genetics · 2026 · 6 claims · 7 setups

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  INSPIRE is a deep-learning method that unifies adversarial learning with a GNN-based encoder and integrated NMF to interpretably integrate multiple spatial transcriptomics datasets

  synthetic dorsolateral brain cortex simulated visium spatial

• CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.

  PMID 41231245 · PMC13053056 · Cancer research · 2026 · 7 claims · 7 setups

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  CanSig is a comprehensive benchmarking tool for evaluating computational methods that identify shared transcriptional signatures in cancer from scRNA-seq data

  neftel kim scrna-seq same scrna-seq ground-truth robustness

• GRNFormer: accurate gene regulatory network inference using graph transformer.

  PMID 41883144 · PMC13069479 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  GRNFormer is a generalizable graph transformer framework for GRN inference from single-cell or bulk transcriptomics data across species, cell types, and platforms without cell-type annotations or prior regulatory information

  hematopoietic embryonic dendritic hepatocytes scrna-seq chip-seq string

• 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