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).

• scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.

  PMID 41746287 · PMC13012890 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 6 setups

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  scDBic improves cell clustering by combining deep autoencoder-based cell clustering, gene clustering, and reverse-strategy identification of key gene clusters

  e-mtab-3321 e-mtab-2600 gse45719 gse52529 biclustering ablation

• S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.

  PMID 41556263 · PMC13042551 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  S3RL is a separable representation learning framework that denoises sparse spatial transcriptomic data and enhances biologically relevant signals by integrating gene expression, spatial coordinates, and histological image features.

  dorsolateral dlpfc nanostring spatial cell-type multi-platform

• Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.

  PMID 41869863 · PMC13170260 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  Among ten/eleven evaluated single-cell FMs, scGPT, Geneformer, and CellFM are the top models considering both performance and user accessibility

  scrna-seq unpaired single-cell scgpt batch multi-omic cell-type

• Prior-guided factorization for reliable imputation of scRNA-seq data.

  PMID 41860953 · PMC13004523 · PLoS computational biology · 2026 · 8 claims · 8 setups

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  scZN models scRNA-seq counts as a mixture of a two-state (Gamma-Poisson/negative binomial) transcriptional bursting process and dropout, formalized via a zero-inflated negative binomial (ZINB) and solved as constrained nonnegative matrix factorization into a cell-to-cell-type assignment matrix and a cell-type expression matrix

  ercc real randomly brain scrna-seq cell-cycle k-nn

• EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.

  PMID 41527854 · PMC12796812 · Briefings in bioinformatics · 2026 · 8 claims · 8 setups

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  EpiXFormer achieves high accuracy (mean AUROC ~0.99) predicting binding sites of both TFs and non-sequence-specific DBPs across 199 DBP-cell type pairs

  dbps tfs common ctcf deep comparative feature-ablation

• scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.

  PMID 41507436 · PMC12859067 · Communications biology · 2026 · 7 claims · 7 setups

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  scXDR outperforms seven methods that transfer drug response information from bulk RNA-seq to single-cell data, across all four evaluated scenarios

  single-cell scrna-seq scxdr gse117872 heterogeneous benchmarking type