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).

• 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

• Comprehensive benchmarking of large language models for RNA secondary structure prediction.

  PMID 40205851 · PMC11982019 · Briefings in bioinformatics · 2025 · 7 claims · 4 setups

  Has reproduction · 88

  Existing RNA-LLMs had not previously been evaluated for secondary structure prediction in a unified, fair experimental setup with the same datasets and prediction model.

  archiveii bprna large-scale secondary cross-family self-supervised

• nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms.

  PMID 15980516 · PMC1160133 · Nucleic acids research · 2005 · 6 claims · 4 setups

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  nsSNPAnalyzer is a web server that predicts whether a query nsSNP is disease-associated or functionally neutral using a Random Forest classifier combining structural and evolutionary information

  curated homologous structure random structural secondary

• Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.

  PMID 16381612 · PMC1360673 · BMC bioinformatics · 2005 · 7 claims · 7 setups

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  A 32-dimensional triplet structure-sequence feature vector combined with SVM (triplet-SVM) can distinguish real human pre-miRNAs from pseudo pre-miRNA hairpins with ~90% accuracy.

  pre-mirnas test conserved-hairpin cross-species secondary svm cross-species

• MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.

  PMID 17553836 · PMC1933124 · Nucleic acids research · 2007 · 8 claims · 8 setups

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  A hybrid feature combining local contiguous triplet structure-sequence composition, MFE of the secondary structure, and P-value of a randomization test improves classification of real vs pseudo pre-miRNAs

  real combined pre-mirna training random independent repeated

• 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

• Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.

  PMID 16757574 · PMC1475747 · Nucleic acids research · 2006 · 6 claims · 8 setups

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  Applying seven independent computational disease-gene prioritization methods in concert to 9556 positional candidate genes identifies a prioritized set of likely T2D and obesity candidate genes

  pubmed genes same randomly geneseeker evoc disease

• Predicting failure rate of PCR in large genomes.

  PMID 18492719 · PMC2441781 · Nucleic acids research · 2008 · 7 claims · 8 setups

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  The number of predicted primer-binding sites in genomic DNA is the most important factor determining PCR failure.

  genomic genome pcr combined large-scale exact-match mismatch-tolerant