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).

• Predicting deleterious nsSNPs: an analysis of sequence and structural attributes.

  PMID 16630345 · PMC1489951 · BMC bioinformatics · 2006 · 8 claims · 7 setups

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  Sequence conservation (PSIC score difference) at the nsSNP position is the single most useful attribute for predicting deleterious vs neutral status.

  swissprot nssnp-containing nssnps machine single-attribute sequence

• PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization.

  PMID 15608166 · PMC540074 · Nucleic acids research · 2005 · 7 claims · 4 setups

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  PA-GOSUB significantly extends the coverage of GO molecular function and subcellular localization annotations for 10 model organism proteomes compared with existing databases (GOA, Swiss-Prot).

  sequences query swiss-prot naive-bayes blast feature

• Genome bioinformatic analysis of nonsynonymous SNPs.

  PMID 17708757 · PMC1978506 · BMC bioinformatics · 2007 · 8 claims · 8 setups

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  Structure- and sequence-based prediction tools can generally distinguish disease-causing mutations from neutral ones

  gene modelled nssnps automated fold relative

• Systematic analysis of human kinase genes: a large number of genes and alternative splicing events result in functional and structural diversity.

  PMID 16351747 · PMC1866387 · BMC bioinformatics · 2005 · 8 claims · 7 setups

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  Systematic in silico search identified 5 novel human kinase genes (on chromosomes 1, 11, 13, 15, 16) and 1 pseudogene (chromosome X) absent from KinBase

  genome genomic kinase psi-blast comparative genscan

• Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

  PMID 19756158 · PMC2742196 · PLoS computational biology · 2009 · 8 claims · 4 setups

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  MIP active learning is a novel active learning method that preferentially seeks informative Positive (functionally active) examples rather than only maximizing classifier accuracy.

  engineered p53 computational yeast-based regional homology