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 prediction models for widespread m6A RNA modification across cell lines and tissues.

  PMID 37995291 · PMC10697738 · Bioinformatics (Oxford, England) · 2023 · 8 claims · 8 setups

  Has reproduction · 79

  CLSM6A is a set of CNN-based deep learning models that predict single-nucleotide-resolution m6A RNA modification sites across eight cell lines and three tissues in H. sapiens

  hct116 a549 brain hek293t miclip m6a-clip-seq m6a-seq

• Predicting the effect of CRISPR-Cas9-based epigenome editing.

  PMID 41524535 · PMC12795505 · eLife · 2026 · 8 claims · 6 setups

  Full-text index only

  Machine learning (CNN and ridge regression) models trained on histone PTM ChIP-seq and RNA-seq data from 13 ENCODE cell types accurately predict endogenous gene expression, with transcriptome-wide correlations of ~0.70-0.79 for most cell types

  hek293t k562 computational chip-seq rna-seq mnase-seq

• A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.

  PMID 41663439 · PMC12996591 · Nature communications · 2026 · 7 claims · 7 setups

  Full-text index only

  Aggregating peaks from 624 high-quality bulk ATAC-seq datasets defines ~1.4 million observed consensus peaks (cPeaks) covering ~30% of the genome.

  bulk scatac-seq cnn gm12878 bulk scatac-seq deep

• Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.

  PMID 41758825 · PMC12948085 · PloS one · 2026 · 8 claims · 6 setups

  Full-text index only

  AI-based predictive models built on unannotated scRNA-seq data (cell-by-gene expression matrices) can classify melanoma patients as ICI responders vs. non-responders

  cd45+ scrna-seq independent scrna-seq single-gene-level pathway-level

• Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.

  PMID 41820396 · PMC13121706 · Nature communications · 2026 · 8 claims · 7 setups

  Full-text index only

  TRIM, a conditional multi-modal variational autoencoder integrating paired scRNAseq and scTCRseq data, predicts T cell clonality and transcriptional states at unmeasured tissue sites/timepoints.

  hnscc colorectal pan-cancer tcr paired umap convolutional