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).

• Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

  PMID 26286638 · PMC4541321 · Scientific reports · 2015 · 6 claims · 4 setups

  Has reproduction · 76

  DRW-GM integrates gene expression and metabolomic profiles via directed random walk on a global gene–metabolite pathway graph to weight genes by topological importance and infer reproducible pathway activities

  prostate global gene metabolomic computational

• Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.

  PMID 34858485 · PMC8631472 · Frontiers in genetics · 2021 · 7 claims · 5 setups

  Has reproduction · 62

  A DNN model using the top 34 betweenness-centrality-ranked hub genes predicts bone metastasis with AUC of 92.11% on the GEO validation data.

  breast degs geo tcga gene protein-protein machine

• CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

  PMID 40739511 · PMC12312500 · BMC biology · 2025 · 8 claims · 8 setups

  Has reproduction · 42

  CanCellCap, a multi-domain learning framework integrating domain adversarial learning and Mixture of Experts, identifies cancer cells across all tissues, cancers, and sequencing platforms by extracting tissue-common and tissue-specific gene expression patterns.

  testing datasets tisch2 prostate scrna-seq simulated spatial

• Diagnostic proteomics: serum proteomic patterns for the detection of early stage cancers.

  PMID 15258335 · PMC3851082 · Disease markers · 2003 · 8 claims · 8 setups

  Full-text index only

  Proteomic pattern analysis of serum mass spectra, without identifying the underlying proteins, can distinguish cancer patients from healthy controls with high sensitivity and specificity.

  serum frozen ig-depleted clinical seldi-tof maldi lc-ms

• scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.

  PMID 40501071 · PMC12159290 · Briefings in bioinformatics · 2025 · 8 claims · 6 setups

  Has reproduction · 62

  scATD enables high-throughput single-cell drug sensitivity prediction for new patients without model parameter retraining via bidirectional Bi-AdaIN style transfer

  panglaodb cancer geo gse137829 single-cell bulk