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

• Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.

  PMID 35794110 · PMC9258765 · Nature communications · 2022 · 8 claims · 5 setups

  Has reproduction · 94

  Deep learning (FFNN-SS and CNN-CBLV) enables accurate and fast likelihood-free estimation of epidemiological parameters and model selection from phylogenies

  simulated same hiv-1 simulation-based bayesian maximum-likelihood

• DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.

  PMID 33662629 · PMC9801043 · Genomics, proteomics & bioinformatics · 2022 · 8 claims · 8 setups

  Has reproduction · 50

  DeeReCT-APA quantitatively predicts the usage of all competing PASs of a gene simultaneously, rather than casting the problem as pairwise comparison like prior methods.

  fibroblast genomic same 3'-region 3'-mrna deep

• How negative sampling shapes the performance of transcription factor binding site prediction models.

  PMID 41601205 · PMC12910371 · Bioinformatics (Oxford, England) · 2026 · 7 claims · 5 setups

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  Negative sampling technique significantly impacts TFBS prediction model performance and interpretation of results

  encode same genome chip-seq atac-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

• 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