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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ChimerDB--a knowledgebase for fusion sequences.
PMID 16381848 · PMC1347382 · Nucleic acids research · 2006 · 8 claims · 6 setups
ChimerDB integrates bioinformatics analysis of mRNA/EST sequences, manually collected literature data, and OMIM translocation data into a single fusion sequence knowledgebase
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Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists.
PMID 16351744 · PMC1866390 · BMC bioinformatics · 2005 · 5 claims · 3 setups
OMIM Clinical Synopsis free-text phenotype and location names can be normalized and hierarchically structured into a controlled vocabulary suitable for computational analysis
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Phenotypic categorization of genetic skin diseases reveals new relations between phenotypes, genes and pathways.
PMID 19744994 · PMC2773259 · Bioinformatics (Oxford, England) · 2009 · 8 claims · 5 setups
560 genetic skin diseases can be decomposed into 71 elementary phenotypic features (42 dermatologic, 29 systemic) that combine to represent each disease as a point in a multidimensional phenotype space
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Mapping proteins to disease terminologies: from UniProt to MeSH.
PMID 18460185 · PMC2367626 · BMC bioinformatics · 2008 · 8 claims · 7 setups
Developed a three-step procedure (disease name extraction, exact matching, partial/similarity-based matching) to map UniProtKB/Swiss-Prot disease names to MeSH terms
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CGMIM: automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes.
PMID 15796777 · PMC1274267 · BMC bioinformatics · 2005 · 8 claims · 2 setups
CGMIM is a Perl program that text-mines OMIM entries to identify cancer-gene associations and genetically-related cancer type pairs.
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InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale.
PMID 17868464 · PMC2375030 · Genome biology · 2007 · 8 claims · 8 setups
InSite predicts protein-pair-specific binding motifs ('Motif M on protein A binds to protein B') by integrating heterogeneous PPI and motif-motif interaction evidence within a Bayesian network trained by EM