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).

• 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

• Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

  PMID 37084271 · PMC10203375 · Bioinformatics (Oxford, England) · 2023 · 6 claims · 8 setups

  Has reproduction · 85

  AlphaFold2-derived structural features (solvent accessibility, amino acid network features, physicochemical environment, pLDDT) can be used to train a random forest classifier (AlphScore) that distinguishes proxy-benign from proxy-pathogenic missense variants.

  gnomad alphafold2-predicted alphscore proteins machine secondary physicochemical

• miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes.

  PMID 16381831 · PMC1347497 · Nucleic acids research · 2006 · 6 claims · 6 setups

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  miRNAMap integrates known miRNA genes from miRBase, literature-curated validated targets, and computationally predicted miRNA genes and targets for human, mouse, rat and dog.

  conserved putative mirna-target normal comparative machine mirna

• Ab initio identification of human microRNAs based on structure motifs.

  PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups

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  MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence

  genome mirna hairpins chromosome secondary linear five-fold

• miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

  PMID 34637468 · PMC8509996 · PloS one · 2021 · 7 claims · 6 setups

  Has reproduction · 100

  RBPs beyond Drosha/DGCR8/Dicer are involved in regulating miRNA biogenesis and explain its spatio-temporal nature

  tissues computational rbps clip-seq rna-seq srna-seq

• 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

• Sequence and structure signatures of cancer mutation hotspots in protein kinases.

  PMID 19834613 · PMC2759519 · PloS one · 2009 · 8 claims · 6 setups

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  Developed CKMD (Composite Kinase Mutation Database), an integrated bioinformatics resource mapping genetic variation in protein kinase genes to sequence, structural, and functional data

  kinase common egfr autoinhibited database-driven structure-informed evolutionary

• Population differentiation and epidemic tracking of Bursaphelenchus xylophilus in China based on chromosome-level assembly and whole-genome sequencing data.

  PMID 34839581 · PMC9300093 · Pest management science · 2022 · 6 claims · 8 setups

  Has reproduction · 44

  Generated the first chromosome-level genome assembly (AH1) of B. xylophilus using PacBio, Illumina, BioNano, and Hi-C data

  xylophilus snp smrt short-read bionano

• Wiggle-predicting functionally flexible regions from primary sequence.

  PMID 16839194 · PMC1500818 · PLoS computational biology · 2006 · 7 claims · 6 setups

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  A GNM-derived, correlation-weighted 'FF score' can objectively define functionally flexible regions (FFRs) that match experimentally confirmed flexible/functional regions (hinges, recognition loops, catalytic loops).

  hiv bovine calmodulin nonredundant gaussian gnm support

• 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