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).

• Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.

  PMID 19901961 · PMC2820592 · Oncogene · 2010 · 8 claims · 6 setups

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  Unsupervised gene expression clustering identifies 3 reproducible molecular subtypes of LMS (Group I/muscle-enriched, Group II, Group III)

  lms independent soft gene array immunohistochemistry

• A gene signature related to programmed cell death to predict immunotherapy response and prognosis in colon adenocarcinoma.

  PMID 39950044 · PMC11823652 · PeerJ · 2025 · 8 claims · 8 setups

  Has reproduction · 73

  COAD patients can be divided into two molecular subtypes (S1, S2) based on 21 prognostic PCD-related genes, with S1 showing worse prognosis and immunosuppressive microenvironment, S2 showing better prognosis and stronger anti-tumor immunity

  coad consensus cibersort mcp-counter

• Leveraging human genomic information to identify nonhuman primate sequences for expression array development.

  PMID 16288651 · PMC1314899 · BMC genomics · 2005 · 8 claims · 6 setups

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  Human genomic DNA sequence can be leveraged to obtain 3' end sequence of NHP orthologs, which can then be used to generate NHP oligonucleotide microarrays

  refseq macaca genomic rhesus exon pcr molecular

• A DNA microarray survey of gene expression in normal human tissues.

  PMID 15774023 · PMC1088941 · Genome biology · 2005 · 6 claims · 6 setups

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  Unsupervised hierarchical clustering of gene expression groups normal tissue samples largely according to anatomic location, cellular composition, or physiologic function.

  normal common computational each two-color comparative unsupervised

• Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

  PMID 19756158 · PMC2742196 · PLoS computational biology · 2009 · 8 claims · 4 setups

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  MIP active learning is a novel active learning method that preferentially seeks informative Positive (functionally active) examples rather than only maximizing classifier accuracy.

  engineered p53 computational yeast-based regional homology