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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Integration of text- and data-mining using ontologies successfully selects disease gene candidates.
PMID 15767279 · PMC1065256 · Nucleic acids research · 2005 · 7 claims · 6 setups
Integrating eVOC anatomical ontology-based text-mining of PubMed abstracts with data-mining of gene expression annotation successfully selects and prioritizes candidate disease genes
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Systematic identification of pseudogenes through whole genome expression evidence profiling.
PMID 16945953 · PMC1636364 · Nucleic acids research · 2006 · 8 claims · 8 setups
Developed a novel bioinformatics method that identifies pseudogenes by profiling whole-genome transcript and protein expression evidence
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Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources.
PMID 16469098 · PMC1409804 · BMC bioinformatics · 2006 · 7 claims · 3 setups
AUGUSTUS+ extends the AUGUSTUS GHMM by combining intrinsic sequence information with extrinsic hints via an extended emission alphabet, so the GHMM jointly models the DNA sequence, gene structure, and hint collection.
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Has reproduction · 45
Identifying and classifying trait linked polymorphisms in non-reference species by walking coloured de bruijn graphs.
PMID 23536903 · PMC3607606 · PloS one · 2013 · 8 claims · 9 setups
Bubbleparse detects sequence variants directly from NGS reads without a reference genome, using the coloured de Bruijn graph implementation of Cortex plus a new depth-first bubble-finding module.
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A model-based approach to selection of tag SNPs.
PMID 16776821 · PMC1525207 · BMC bioinformatics · 2006 · 7 claims · 5 setups
The Li and Stephens hidden Markov model outperforms other tested models (simple Markov, two-state HMM, HMM-4D, greedy GR-1/GR-2) in description code-length, tag set information content, and prediction of tagged SNPs.
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Has reproduction · 83
Accurate prediction of metagenome-assembled genome completeness by MAGISTA, a random forest model built on alignment-free intra-bin statistics.
PMID 35248155 · PMC8898458 · Environmental microbiome · 2022 · 7 claims · 7 setups
MAGISTA, a random forest model built on alignment-free intra-bin distance-distribution statistics, can estimate MAG completeness and purity without relying on reference marker genes.