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).
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A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise.
PMID 18615428 · PMC2855839 · Proteomics · 2008 · 6 claims · 4 setups
Noise in SELDI-TOF/MALDI-TOF mass spectrometry data is heteroscedastic across the m/z range, with larger variance at lower m/z values, contrary to the homogeneous noise assumption of existing wavelet denoising methods.
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Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.
PMID 19055809 · PMC2657802 · BMC bioinformatics · 2008 · 7 claims · 5 setups
QC processes for identifying and removing low-quality spectra are often overlooked in proteomic profiling studies
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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
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.
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Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.
PMID 19043545 · PMC2581785 · PLoS genetics · 2008 · 7 claims · 4 setups
A GWA study using serum metabolomics identifies SNPs associated with metabolite concentrations, explaining up to 12% of variance for single metabolites
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Development of proteomic patterns for detecting lung cancer.
PMID 14757945 · PMC3851077 · Disease markers · 2003 · 8 claims · 3 setups
A decision tree classification algorithm built on three serum protein mass peaks (8122Da, 1452Da, 1610Da) can discriminate lung cancer patients from healthy controls
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Independent component analysis reveals new and biologically significant structures in micro array data.
PMID 16762055 · PMC1557674 · BMC bioinformatics · 2006 · 7 claims · 8 setups
ICA applied to three microarray datasets reveals many biologically significant components, including low-ranking ones not obvious by rank alone