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).

• Serum diagnosis of diffuse large B-cell lymphomas and further identification of response to therapy using SELDI-TOF-MS and tree analysis patterning.

  PMID 18163913 · PMC2242801 · BMC cancer · 2007 · 8 claims · 8 setups

  Full-text index only

  SELDI-TOF-MS serum proteomic patterns analyzed by decision tree classification (Biomarker Pattern Software) can discriminate DLBCL patients from healthy controls with high sensitivity and specificity.

  serum seldi dlbcl pooled seldi-tof-ms proteinchip decision

• Development of proteomic patterns for detecting lung cancer.

  PMID 14757945 · PMC3851077 · Disease markers · 2003 · 8 claims · 3 setups

  Full-text index only

  A decision tree classification algorithm built on three serum protein mass peaks (8122Da, 1452Da, 1610Da) can discriminate lung cancer patients from healthy controls

  serum pooled seldi-tof-ms decision reproducibility

• Application of serum SELDI proteomic patterns in diagnosis of lung cancer.

  PMID 16029516 · PMC1183195 · BMC cancer · 2005 · 6 claims · 2 setups

  Full-text index only

  A five-protein-peak SELDI decision-tree pattern (11493, 6429, 8245, 5335, 2538 Da) distinguishes lung cancer sera from healthy control sera with relatively high sensitivity and specificity

  serum seldi-tof-ms electrochemiluminescent

• Decision forest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer; a novel method.

  PMID 16026601 · PMC1637030 · BMC bioinformatics · 2005 · 8 claims · 2 setups

  Full-text index only

  DF-SNPs, a novel adaptation of the Decision Forest method, can classify esophageal cancer cases vs. controls based on SNP genotype data with high concordance, sensitivity, and specificity.

  genomic snp high-throughput decision

• A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

  PMID 19772600 · PMC2765429 · Journal of translational medicine · 2009 · 7 claims · 3 setups

  Full-text index only

  The naive Bayes model with the wrapper-based feature selection approach performed best among all predictive models tested for distinguishing CFS from controls.

  subjects naive svm c4.5

• A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study.

  PMID 37852185 · PMC10591064 · Cell reports. Medicine · 2023 · 8 claims · 8 setups

  Has reproduction · 65

  A 3-lncRNA urine extracellular vesicle classifier (Clnc: AC015987.1, CTD-2589M5.4, RP11-363E6.3) detects high-grade PCa with higher accuracy than PCA3, mpMRI, PCPT-RC 2.0, and ERSPC-RC

  urinary tcga paired urine sequencing lncrna qrt-pcr