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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Has reproduction · 82
SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis.
PMID 30698691 · PMC6354025 · GigaScience · 2019 · 8 claims · 5 setups
SMAGEXP integrates the metaMA and metaRNASeq R packages into Galaxy to provide a unified tool suite for transcriptomics meta-analysis.
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Has reproduction · 100
Exploring Gene Expression Patterns in Alzheimer's Disease Using a Human Microarray Data Meta-Analysis.
PMID 41744654 · PMC12938635 · Biology · 2026 · 6 claims · 7 setups
AD brains show a distinct transcriptomic profile with up-regulation of immune/inflammation genes and down-regulation of synapse/neuronal-signaling genes
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Has reproduction · 80
Curation of over 10 000 transcriptomic studies to enable data reuse.
PMID 33599246 · PMC7904053 · Database : the journal of biological databases and curation · 2021 · 8 claims · 6 setups
Gemma is a curated database and bioinformatics system that addresses metadata, probe annotation, and expression data inconsistencies in GEO to enable transcriptomic data reuse
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Full-text index only
Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival.
PMID 18778477 · PMC2553089 · BMC medicine · 2008 · 7 claims · 7 setups
A custom BOAC SNP panel of 3404 SNPs in 983 genes was developed using the Affymetrix GeneChip Targeted Genotyping Platform, focused on non-synonymous coding SNPs and regulatory-region SNPs in candidate genes.
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Has reproduction · 51
SGCP: a spectral self-learning method for clustering genes in co-expression networks.
PMID 38956463 · PMC11221046 · BMC bioinformatics · 2024 · 7 claims · 4 setups
SGCP, a spectral self-learning method, yields gene co-expression modules with higher GO enrichment than WGCNA, CoExpNets, and CEMiTool across 12 real gene expression datasets.