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).

• Modeling nascent transcription from chromatin landscape and structure with CLASTER.

  PMID 41691282 · PMC13011747 · Genome biology · 2026 · 7 claims · 8 setups

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  CLASTER, a deep neural network combining chromatin landscape tracks and 3D contact maps, accurately predicts kilobasepair-resolution nascent RNA (EU-seq) profiles in a DNA-sequence-agnostic manner

  embryonic mescs chromatin k562 eu-seq chip-seq micro-c

• Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution.

  PMID 41980945 · PMC13250166 · Nature communications · 2026 · 8 claims · 8 setups

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  Hi-Compass predicts cell-type-specific Hi-C contact maps using only ATAC-seq as cell-type-specific input, plus DNA sequence and a generalized CTCF binding profile

  gm12878 curated bulk training hi-c ctcf matched

• 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

• SiCmiR Atlas: Single-Cell miRNA Landscape Reveals Hub-miRNA and Network Signatures in Human Cancers.

  PMID 41691474 · PMC13042402 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026 · 8 claims · 8 setups

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  SiCmiR, a two-layer neural network, predicts mature miRNA expression profiles from only 977 LINCS L1000 landmark genes

  tcga k562 pancreatic acth-secreting paired bulk scrna-seq-based

• A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

  PMID 41923359 · PMC13090826 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups

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  LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data

  lncrna hypertrophic gencode lncrna-protein-mirna mlp-based independent ablation

• 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

• The protein-phosphatome of the human malaria parasite Plasmodium falciparum.

  PMID 18793411 · PMC2559854 · BMC genomics · 2008 · 8 claims · 8 setups

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  P. falciparum possesses 27 putative protein phosphatase sequences across the four major PP families (PPP, PPM, PTP, NIF), plus 7 additional sequences predicted to dephosphorylate non-protein substrates, totaling 34.

  sapiens ppp-domain metallophosphatase pf14_0142 hmm sequence neighbour-net

• A unified framework for correcting batch effects and integrating multi-omics data.

  PMID 41786846 · PMC13079841 · Scientific reports · 2026 · 7 claims · 6 setups

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  MoDAmix, a four-stage domain adaptation framework (pre-training, single-omics adversarial adaptation, multi-omics adversarial alignment, semi-supervised class alignment), unifies batch correction across multiple omics layers.

  brain aml same single-cell bulk k-means

• Dual mechanisms of supporting cell regeneration in the neonatal mouse cochlea.

  PMID 41867623 · PMC13000480 · iScience · 2026 · 8 claims · 8 setups

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  GER cells regenerate IPhCs through both mitotic and non-mitotic mechanisms

  neonatal lgr5dtr ki67creert2 epcam+ whole-mount edu genetic