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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GoMiner: a resource for biological interpretation of genomic and proteomic data.
PMID 12702209 · PMC154579 · Genome biology · 2003 · 8 claims · 4 setups
GoMiner organizes 'interesting' gene lists (e.g., differentially expressed genes) into the Gene Ontology hierarchy for biological interpretation, displaying results as both a tree and a directed acyclic graph (DAG).
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Characterization of the human DYRK1A promoter and its regulation by the transcription factor E2F1.
PMID 18366763 · PMC2292204 · BMC molecular biology · 2008 · 8 claims · 8 setups
Transcription start sites of human DYRK1A are distributed over an 800 bp region within an unmethylated CpG island
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Proteomic interrogation of androgen action in prostate cancer cells reveals roles of aminoacyl tRNA synthetases.
PMID 19763266 · PMC2740864 · PloS one · 2009 · 8 claims · 8 setups
Androgen treatment alters the whole-cell proteome of LNCaP prostate cancer cells
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Has reproduction · 70
Extensive androgen receptor enhancer heterogeneity in primary prostate cancers underlies transcriptional diversity and metastatic potential.
PMID 36450752 · PMC9712620 · Nature communications · 2022 · 8 claims · 8 setups
AR enhancer/chromatin binding usage is highly heterogeneous between primary prostate tumors, with <5% of all AR binding sites shared by half of tumors analyzed.
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The cancer secretome: a reservoir of biomarkers.
PMID 18796163 · PMC2562990 · Journal of translational medicine · 2008 · 8 claims · 8 setups
Cancer secretome analysis is a promising reservoir for identifying novel, non-invasive cancer biomarkers, addressing limitations of whole blood/serum proteomics
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Has reproduction · 67
Optimal scaling of digital transcriptomes.
PMID 24223126 · PMC3819321 · PloS one · 2013 · 8 claims · 8 setups
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.