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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Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.
PMID 18588693 · PMC2446391 · BMC bioinformatics · 2008 · 8 claims · 5 setups
A decision tree built on 10 selected sequence-derived features classifies SAPs as Disease or Polymorphism with 82.6% accuracy and 0.607 MCC in cross-validation.
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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
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.
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Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.
PMID 15207009 · PMC449699 · BMC bioinformatics · 2004 · 8 claims · 8 setups
New classifiers (SVMs, ensembles) and new wavelet-derived (Gabor, Daubechies) features improve recognition of protein subcellular location patterns over the previous neural network approach
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Towards precise classification of cancers based on robust gene functional expression profiles.
PMID 15774002 · PMC1274255 · BMC bioinformatics · 2005 · 6 claims · 7 setups
Functional expression profiles (FEPs) achieve comparable or better classification performance than conventional gene expression profiles (GEPs) across four public microarray datasets
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Has reproduction · 65
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
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
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Has reproduction · 48
Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a hybrid signature.
PMID 35143511 · PMC8830616 · PloS one · 2022 · 8 claims · 6 setups
A hybrid signature combining the Nottingham Prognostic Index with SIS-selected gene expressions can be built in a data-driven fashion (NPI treated as a gene expression during feature selection).
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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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Speeding disease gene discovery by sequence based candidate prioritization.
PMID 15766383 · PMC1274252 · BMC bioinformatics · 2005 · 7 claims · 8 setups
Disease genes (OMIM) differ significantly from non-disease genes in sequence-based features including gene/cDNA/protein size, exon number, homolog conservation, secretion signal, 3' UTR length, CpG islands, and distance to nearest gene.
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Machine-learning approaches for classifying haplogroup from Y chromosome STR data.
PMID 18551166 · PMC2396484 · PLoS computational biology · 2008 · 8 claims · 5 setups
Y-STR allelic variability is partitioned more by differences among haplogroups than by differences among populations, suggesting Y-STRs carry haplogroup information
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Ab initio identification of human microRNAs based on structure motifs.
PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups
MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence
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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
The naive Bayes model with the wrapper-based feature selection approach performed best among all predictive models tested for distinguishing CFS from controls.