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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Discovery and identification of potential biomarkers of papillary thyroid carcinoma.
PMID 19785722 · PMC2761863 · Molecular cancer · 2009 · 8 claims · 7 setups
A 3-peak (m/z 9190, 6631, 8697 Da) SVM classification model discriminates PTC from non-cancer controls with high sensitivity and specificity
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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
The previously reported classifier (m/z 8,132 and 4,002) failed to discriminate CRC patients from healthy volunteers in this independent validation cohort
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Application of serum SELDI proteomic patterns in diagnosis of lung cancer.
PMID 16029516 · PMC1183195 · BMC cancer · 2005 · 6 claims · 2 setups
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
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Proteomics as a tool for biomarker discovery.
PMID 18057524 · PMC3851415 · Disease markers · 2007 · 8 claims · 7 setups
A useful clinical biomarker must be easily attainable, have adequate sensitivity, have adequate specificity, and lead to patient benefit through intervention
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The cancer secretome: a reservoir of biomarkers.
PMID 18796163 · PMC2562990 · Journal of translational medicine · 2008 · 8 claims · 8 setups
Cancer secretome analysis is a promising reservoir for identifying novel, non-invasive cancer biomarkers, addressing limitations of whole blood/serum proteomics
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Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data.
PMID 19733729 · PMC2748225 · Analytica chimica acta · 2009 · 8 claims · 4 setups
ACA-based wavelet coefficient feature selection can achieve up to 100% classification accuracy on training, validating, and independent testing sets using only 5 selected features.
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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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Constructing support vector machine ensembles for cancer classification based on proteomic profiling.
PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups
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.
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Contribution of oncoproteomics to cancer biomarker discovery.
PMID 17407558 · PMC1852117 · Molecular cancer · 2007 · 6 claims · 8 setups
Oncoproteomic biomarker panels (2-DE/MS-based) show higher sensitivity and specificity than currently used single tumor markers across multiple cancer types (Table 1)
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Breast cancer proteomics reveals correlation between estrogen receptor status and differential phosphorylation of PGRMC1.
PMID 18922159 · PMC2614521 · Breast cancer research : BCR · 2008 · 8 claims · 5 setups
PGRMC1 protein spots are differentially abundant between ER-negative and ER-positive breast tumors, with two of three spots more abundant in ER-negative tumors
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MALDI profiling of human lung cancer subtypes.
PMID 19890392 · PMC2767501 · PloS one · 2009 · 8 claims · 8 setups
PIMAC/MALDI-TOF peptide profiles combined with classification models can distinguish normal lung from tumor and differentiate NSCLC histological subtypes
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Proteomic interrogation of androgen action in prostate cancer cells reveals roles of aminoacyl tRNA synthetases.
PMID 19763266 · PMC2740864 · PloS one · 2009 · 8 claims · 8 setups
Androgen treatment alters the whole-cell proteome of LNCaP prostate cancer cells
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Diagnostic proteomics: serum proteomic patterns for the detection of early stage cancers.
PMID 15258335 · PMC3851082 · Disease markers · 2003 · 8 claims · 8 setups
Proteomic pattern analysis of serum mass spectra, without identifying the underlying proteins, can distinguish cancer patients from healthy controls with high sensitivity and specificity.
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Feature selection and nearest centroid classification for protein mass spectrometry.
PMID 15788095 · PMC1274262 · BMC bioinformatics · 2005 · 8 claims · 5 setups
Sequential forward selection and a novel boosted feature selection algorithm produce the most consistent classification results across all five cancer data sets tested
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Characterisation and protein expression profiling of annexins in colorectal cancer.
PMID 18071363 · PMC2361450 · British journal of cancer · 2008 · 7 claims · 5 setups
Annexins A1, A2, A4 and A11 are overexpressed in primary colorectal cancer compared with normal colon
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Tubulin proteomics: towards breaking the code.
PMID 18840397 · PMC4039029 · Analytical biochemistry · 2009 · 8 claims · 8 setups
Tubulin isotype and posttranslational-modification diversity constitutes a 'tubulin code' that is read by microtubule-associated proteins and translates into specific in vivo functions
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On consensus biomarker selection.
PMID 17570864 · PMC1892093 · BMC bioinformatics · 2007 · 7 claims · 2 setups
Four popular feature ranking criteria (t-statistic, mutual information, peak probability contrasts, random forest variable importance) produce different rankings of the same features on the same dataset.
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A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.
PMID 19828085 · PMC2762074 · BMC bioinformatics · 2009 · 7 claims · 1 setups
A scale-space based unsupervised feature extraction method combined with SVM classification achieves high accuracy in ovarian cancer detection from serum mass spectra.
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Proteomics: characterizing the cogs in the machinery of life.
PMID 14630521 · PMC1241753 · Environmental health perspectives · 2003 · 8 claims · 5 setups
Protein expression patterns in blood serum, detected via SELDI-TOF mass spectrometry and analyzed with a genetic algorithm, can distinguish ovarian cancer patients from healthy individuals with very high sensitivity and specificity.