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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Vertebrate gene finding from multiple-species alignments using a two-level strategy.
PMID 16925840 · PMC1810555 · Genome biology · 2006 · 8 claims · 5 setups
DOGFISH cleanly separates a multi-species alignment classifier (RVM cascade) from an HMM-based structure predictor, avoiding tight coupling of alignment complexity with HMM formalism
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CanPredict: a computational tool for predicting cancer-associated missense mutations.
PMID 17537827 · PMC1933186 · Nucleic acids research · 2007 · 8 claims · 7 setups
CanPredict is a web application providing public access to a random forest classifier that combines SIFT, LogR.E-value, and GOSS scores to predict whether a missense mutation is cancer-associated
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Non-EST-based prediction of novel alternatively spliced cassette exons with cell signaling function in Caenorhabditis elegans and human.
PMID 17452356 · PMC1904267 · Nucleic acids research · 2007 · 8 claims · 7 setups
PASE (Prediction of Alternative Signaling Exons) is a computational algorithm combining Markov splice-site models, a Bayesian classifier, species conservation, and Scansite motif scoring to identify novel alternative cassette exons involved in cell signaling.
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In silico analysis of missense substitutions using sequence-alignment based methods.
PMID 18951440 · PMC3431198 · Human mutation · 2008 · 8 claims · 7 setups
Carefully validated PMSA-based computational algorithms can achieve predictive values of ~75-95% for classifying missense substitutions as pathogenic or neutral.
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Extraction of human kinase mutations from literature, databases and genotyping studies.
PMID 19758464 · PMC2745582 · BMC bioinformatics · 2009 · 7 claims · 6 setups
A literature mining pipeline combining MutationFinder, false-positive filtering, and SVM-based classification can extract and disambiguate single-point mutation mentions from abstracts and full text