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).

• Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

  PMID 32825264 · PMC7563725 · Biomolecules · 2020 · 6 claims · 7 setups

  Has reproduction · 80

  Combining WGCNA-derived hub genes with a VAE-derived 10-dimensional representation as features for an SVM classifier achieves high accuracy (0.9692) and AUC (0.9981) for colorectal cancer prediction.

  colorectal combined differentially crc microarray differential weighted

• Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.

  PMID 39754813 · PMC11754497 · Translational oncology · 2025 · 6 claims · 8 setups

  Has reproduction · 78

  A consensus WGCNA approach combining matched tumor-organoid and independent organoid drug-response expression data identifies gene modules and hub genes predictive of 5-FU chemotherapy response

  colorectal matched combined expression microarray rna-seq consensus

• AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes.

  PMID 17485433 · PMC5947781 · Bioinformatics (Oxford, England) · 2007 · 7 claims · 2 setups

  Full-text index only

  AutoCSA is an automated algorithm, extended from the CSA protocol, that detects DNA sequence variants in cancer genomes with minimal manual intervention

  training kinase capillary targeted

• CanPredict: a computational tool for predicting cancer-associated missense mutations.

  PMID 17537827 · PMC1933186 · Nucleic acids research · 2007 · 8 claims · 7 setups

  Full-text index only

  CanPredict is a web application providing public access to a random forest classifier that combines SIFT, LogR.E-value, and GOSS scores to predict whether a missense mutation is cancer-associated

  missense observed sjoblom refseq cross-validation recurrence comparative

• Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.

  PMID 19654296 · PMC2763410 · Cancer research · 2009 · 7 claims · 7 setups

  Full-text index only

  CHASM, a Random Forest-based computational method, was developed to identify and prioritize missense mutations likely to be functional drivers of tumor cell proliferation.

  missense same synthetic tp53 random support principal