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).

• iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.

  PMID 41775921 · PMC13066597 · Communications biology · 2026 · 8 claims · 5 setups

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  iAODE combines a ZINB-likelihood VAE, a latent Neural ODE, low-weight KL regularization, and an interpretable reconstruction (irecon) bottleneck to learn generative, temporally continuous latent spaces for scATAC-seq.

  simulated real small simulated scatac-seq

• CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.

  PMID 41734268 · PMC12987762 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 5 setups

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  CIRCE re-implements the Cicero co-accessibility algorithm in Python, producing near-identical results while running much faster and using far less memory

  bmmc pbmc foetal cis-regulatory enhancer-promoter scalability

• Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.

  PMID 41530870 · PMC12888741 · Genome medicine · 2026 · 8 claims · 8 setups

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  MAAS integrates chromatin accessibility, CNVs, and SNVs from scATAC-seq data to identify functional tumor cell subpopulations

  tumor pediatric b-cell glioblastoma scatac-seq bulk gene

• maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.

  PMID 36719906 · PMC9917285 · PLoS computational biology · 2023 · 8 claims · 6 setups

  Has reproduction · 98

  maxATAC is a suite of deep neural network models enabling state-of-the-art, genome-scale TFBS prediction from ATAC-seq, with models for 127 human transcription factors

  lines hepg2 types single chip-seq omni-atac-seq atac-seq

• CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.

  PMID 41555433 · PMC12903630 · Genome biology · 2026 · 8 claims · 8 setups

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  CLAMP, a chromatin-accessibility language model, predicts protein-mediated chromatin loops across 10 species, 18 proteins, and 24 cell types with superior performance versus existing methods.

  species gm12878 types chromatin cross-protein cross-cell-type

• Widespread allele-specific topological domains in the human genome are not confined to imprinted gene clusters.

  PMID 36869353 · PMC9983196 · Genome biology · 2023 · 8 claims · 5 setups

  Has reproduction · 64

  HiCFlow, a new bioinformatic pipeline, performs de novo haplotype assembly, phasing, and visualization of allele-specific (parental) chromatin conformation directly from Hi-C data without requiring pre-phased haplotypes.

  1-7hb2 gm12878 imr-90 lines region hi-c genome-wide

• 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

• 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

• Predicting enhancers in mammalian genomes using supervised hidden Markov models.

  PMID 30917778 · PMC6437899 · BMC bioinformatics · 2019 · 8 claims · 8 setups

  Has reproduction · 70

  eHMM predicts enhancers with high precision and recall comparable to state-of-the-art methods and consistently outperforms them in accuracy and resolution

  esc atac-seq chip-seq cage

• Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.

  PMID 41821037 · PMC12983708 · Genome biology · 2026 · 8 claims · 8 setups

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  Horizontal integration of scRNA-seq and snRNA-seq improves cell-type identification

  renal kidney pbmc heart scrna-seq snrna-seq snatac-seq