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).

• STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer.

  PMID 32323921 · PMC7178451 · Molecular systems biology · 2020 · 6 claims · 4 setups

  Has reproduction · 66

  Low STAT3 expression in primary prostate cancer is associated with up-regulated OXPHOS and ribosomal biosynthesis at the transcriptomic level.

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• Epigenetic loss of heterogeneity from low to high grade localized prostate tumours.

  PMID 34911933 · PMC8674326 · Nature communications · 2021 · 7 claims · 4 setups

  Has reproduction · 79

  Shared chromatin accessibility features among low-grade (Gleason pattern 3) prostate cancer cells are lost in high-grade (Gleason pattern 4) tumours.

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• Systematic identification of ACE2 expression modulators reveals cardiomyopathy as a risk factor for mortality in COVID-19 patients.

  PMID 35012625 · PMC8743438 · Genome biology · 2022 · 7 claims · 8 setups

  Has reproduction · 68

  GENEVA is a semi-automated, study-design-agnostic framework that mines large-scale public RNA-seq data to identify conditions modulating a gene of interest's expression

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• Correcting scale distortion in RNA sequencing data.

  PMID 39875825 · PMC11776150 · BMC bioinformatics · 2025 · 8 claims · 8 setups

  Has reproduction · 76

  Local averaging reveals expression-level-dependent biases that differ from sample to sample across all RNA-seq datasets studied, and are not corrected by conventional normalization (TPM/FPKM)

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• Optimal scaling of digital transcriptomes.

  PMID 24223126 · PMC3819321 · PloS one · 2013 · 8 claims · 8 setups

  Has reproduction · 67

  Fifteen existing and novel transcript-count normalization algorithms can be compared with two novel, mutually independent metrics: the number of "uniform" genes (sufficiently low coefficient of variation after normalization) and low average Spearman correlation between normalized expression profiles of gene pairs.

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• scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.

  PMID 40501071 · PMC12159290 · Briefings in bioinformatics · 2025 · 8 claims · 6 setups

  Has reproduction · 62

  scATD enables high-throughput single-cell drug sensitivity prediction for new patients without model parameter retraining via bidirectional Bi-AdaIN style transfer

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