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).

• BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization.

  PMID 41266599 · PMC12635123 · Communications biology · 2025 · 8 claims · 8 setups

  Has reproduction · 50

  BiRNA-BERT uses adaptive dual-tokenization that dynamically selects nucleotide-level (NUC) or byte-pair encoding (BPE) tokens based on input sequence length

  rfam plant lncrna-mirna sequences unsupervised mirna-lncrna length-binned

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

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

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  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

• 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

• 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

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  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

• AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.

  PMID 41792137 · PMC13087051 · Nature communications · 2026 · 7 claims · 8 setups

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  AMR-GNN, a graph neural network integrating multiple genomic representations (unitigs, SNPs, FCGR) via low-rank multimodal fusion, improves AMR phenotype prediction in P. aeruginosa compared to single-representation baseline models.

  pseudomonas aeruginosa escherichia coli whole-genome elastic convolutional