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).

• High genetic variability of HIV-1 in female sex workers from Argentina.

  PMID 17697319 · PMC1971708 · Retrovirology · 2007 · 7 claims · 6 setups

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  HIV-1 genetic diversity among Argentine FSWs is extensive, with BF recombinants predominating over subtypes B and C

  pbmc-derived aligned near pro heteroduplex pcr near

• The specificity and polymorphism of the MHC class I prevents the global adaptation of HIV-1 to the monomorphic proteasome and TAP.

  PMID 18949050 · PMC2569417 · PloS one · 2008 · 6 claims · 5 setups

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  Within individual hosts, proteasome and TAP escape mutations in HIV-1 occur frequently

  hiv-1 hla-genotyped predicted bioinformatic longitudinal ancestral

• Stability analysis of mixtures of mutagenetic trees.

  PMID 18366778 · PMC2335279 · BMC bioinformatics · 2008 · 7 claims · 5 setups

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  Mutagenetic trees mixture models capture multiple alternative pathways of ordered accumulation of genetic events (e.g., HIV resistance mutations, cancer chromosomal aberrations).

  simulated hiv-1 hiv timed simulation genotypic mutagenetic

• Antigenic diversity, transmission mechanisms, and the evolution of pathogens.

  PMID 19847288 · PMC2759524 · PLoS computational biology · 2009 · 8 claims · 3 setups

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  Three distinct infection types (A, B, C) emerge as maxima in the pathogen fitness landscape, each with characteristic within-host dynamics, contact network structure, and transmission mode

  generic simulated hiv-1 stochastic dynamic regression

• Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.

  PMID 35794110 · PMC9258765 · Nature communications · 2022 · 8 claims · 5 setups

  Has reproduction · 94

  Deep learning (FFNN-SS and CNN-CBLV) enables accurate and fast likelihood-free estimation of epidemiological parameters and model selection from phylogenies

  simulated same hiv-1 simulation-based bayesian maximum-likelihood