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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Independent component analysis reveals new and biologically significant structures in micro array data.
PMID 16762055 · PMC1557674 · BMC bioinformatics · 2006 · 7 claims · 8 setups
ICA applied to three microarray datasets reveals many biologically significant components, including low-ranking ones not obvious by rank alone
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Has reproduction · 71
Machine Learning-Based Integrated Analysis of PANoptosis Patterns in Acute Myeloid Leukemia Reveals a Signature Predicting Survival and Immunotherapy.
PMID 38322112 · PMC10846924 · International journal of clinical practice · 2024 · 7 claims · 7 setups
AML patients can be categorized into two distinct PANRG-based clusters with differing prognosis and immune characteristics
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Comparing protein abundance and mRNA expression levels on a genomic scale.
PMID 12952525 · PMC193646 · Genome biology · 2003 · 8 claims · 8 setups
Correlations between mRNA expression and protein abundance are generally poor or limited across most studies reviewed, including in yeast and human cancers
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IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation.
PMID 19752006 · PMC2808259 · Molecular & cellular proteomics : MCP · 2010 · 6 claims · 5 setups
IDEAL-Q predicts the elution time of peptides unidentified in a given LC-MS/MS run (but identified in others) using a computation-efficient linear regression plus fragmental refining function, avoiding costly whole-dataset pattern recognition
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Has reproduction · 76
Correcting scale distortion in RNA sequencing data.
PMID 39875825 · PMC11776150 · BMC bioinformatics · 2025 · 8 claims · 8 setups
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)