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).

• A focused antibody library for selecting scFvs expressed at high levels in the cytoplasm.

  PMID 18034894 · PMC2241821 · BMC biotechnology · 2007 · 7 claims · 7 setups

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  A human scFv library was built on the single scFv13R4 framework with CDR3 loops diversified to mimic natural human CDR3 amino-acid distributions.

  coli hela filamentous scfv gfp sds-page fluorescence

• Identification of genes influencing the evolution of Escherichia coli ST372 in dogs and humans.

  PMID 36752777 · PMC9997745 · Microbial genomics · 2023 · 8 claims · 8 setups

  Has reproduction · 89

  Dogs are the dominant host of E. coli ST372, and clusters within the ST372 population structure exhibit distinctive O:H types.

  coli st372 core draft whole-genome core population

• A consensus approach to vertebrate de novo transcriptome assembly from RNA-seq data: assembly of the duck (Anas platyrhynchos) transcriptome.

  PMID 25009556 · PMC4070175 · Frontiers in genetics · 2014 · 8 claims · 8 setups

  Has reproduction · 67

  Multiple k-mer (MK) assemblies are more complete than single k-mer (SK) assemblies, showing higher reads-mapped-back-to-transcripts (RMBT) and higher CEGMA complete-gene percentages for all three tools.

  anas duck million random illumina read novo

• miRGen 2.0: a database of microRNA genomic information and regulation.

  PMID 19850714 · PMC2808909 · Nucleic acids research · 2010 · 7 claims · 6 setups

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  miRGen 2.0 is a database providing comprehensive information about the genomic position of human and mouse microRNA coding transcripts and their regulation by transcription factors

  lung organ malme embryonic polii small nucleosome

• 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