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).
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Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity.
PMID 41950003 · PMC13244359 · Cell reports · 2026 · 8 claims · 7 setups
Multi-omics factor analysis (MOFA) integrating seven omics views from 1,358 ROS/MAP participants identifies cross-omics biological factors relating to AD phenotypes.
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PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic gold standards.
PMID 42054465 · PMC13127566 · Science advances · 2026 · 8 claims · 4 setups
PSGRN infers GRNs by generating pseudoannotations from gene-gene correlations and iteratively refining them via a self-training classifier using pre/post-intervention expression features.
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GRNFormer: accurate gene regulatory network inference using graph transformer.
PMID 41883144 · PMC13069479 · Bioinformatics (Oxford, England) · 2026 · 8 claims · 8 setups
GRNFormer is a generalizable graph transformer framework for GRN inference from single-cell or bulk transcriptomics data across species, cell types, and platforms without cell-type annotations or prior regulatory information
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Placental gene signatures associated with high neonatal adiposity: role for immune cell activation.
PMID 41958865 · PMC13061145 · Journal of the Endocrine Society · 2026 · 8 claims · 8 setups
A placental transcriptomic signature is associated with high neonatal adiposity
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CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.
PMID 42059479 · PMC13130071 · Briefings in bioinformatics · 2026 · 7 claims · 5 setups
CaHoT-GRN integrates pretrained biological language model embeddings (DNABERT for DNA, ESM for protein) with scRNA-seq expression data to improve GRN inference
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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
LncADeep 2.0 outperforms LncADeep and other existing tools for lncRNA identification on both GENCODE annotated transcripts and independent RNA-seq data