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

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  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

• Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.

  PMID 18923431 · PMC2692715 · Genes and immunity · 2009 · 7 claims · 6 setups

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  A two-stage strategy (Random Forest filtering followed by decision tree modeling) can integrate categorical genetic and continuous proteomic data to identify biomarkers of AE risk

  ebv-transformed serum combined rf-filtered snp serum random

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

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

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  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

• Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

  PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups

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  CSVME, built by selecting a subset of base SVMs via SVM-RFE ranking and fusing them with a trained upper-layer SVM, achieves better classification performance than an ensemble of all base SVMs.

  serum computational seldi-tof svm-rfe-based 10-fold

• Limitations in SELDI-TOF MS whole serum proteomic profiling with IMAC surface to specifically detect colorectal cancer.

  PMID 19689818 · PMC2743709 · BMC cancer · 2009 · 7 claims · 3 setups

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  The previously reported classifier (m/z 8,132 and 4,002) failed to discriminate CRC patients from healthy volunteers in this independent validation cohort

  serum pooled seldi-tof comparative decision