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).

• The use of edge-betweenness clustering to investigate biological function in protein interaction networks.

  PMID 15740614 · PMC555937 · BMC bioinformatics · 2005 · 8 claims · 7 setups

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  Edge-Betweenness clustering separates protein interaction graphs into subgraphs whose GO term distributions show significant correlations, revealing biologically meaningful functional modules.

  interaction yeast edge-betweenness chi-squared robustness

• Representing and querying disease networks using graph databases.

  PMID 27462371 · PMC4960687 · BioData mining · 2016 · 7 claims · 8 setups

  Has reproduction · 77

  Graph databases are well suited for representing biological information that is highly connected, semi-structured, and unpredictable.

  uniprot proteome asthma tissues all-versus-all graph protein-protein

• Structure of protein interaction networks and their implications on drug design.

  PMID 19876376 · PMC2760708 · PLoS computational biology · 2009 · 8 claims · 6 setups

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  Budding yeast and human PINs are scale-rich and configured as highly optimized tolerance (HOT) networks similar to Internet router-level topology, rather than scale-free networks formed by preferential attachment.

  budding pin yeast protein-protein graph-theoretic gene

• VIRGO: computational prediction of gene functions.

  PMID 16845022 · PMC1538839 · Nucleic acids research · 2006 · 8 claims · 6 setups

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  VIRGO constructs a functional linkage network (FLN) from gene expression and molecular interaction data, labels genes with GO annotations, and propagates these labels to predict functions of unlabelled genes

  cerevisiae sapiens leave-one-out gene protein-protein

• Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information.

  PMID 15941473 · PMC1164402 · BMC bioinformatics · 2005 · 8 claims · 5 setups

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  A combined linguistic+statistical+rule-based method achieves precision 0.61 and recall 0.97 (f=0.74) detecting yeast protein-protein interactions across 12,300 Medline abstracts.

  yeast yeast-related interaction random dictionary-based manual graph

• Ontological visualization of protein-protein interactions.

  PMID 15707487 · PMC550656 · BMC bioinformatics · 2005 · 8 claims · 8 setups

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  Aggregating independently made GO 'protein binding' (IPI) annotations reveals larger, previously undescribed mouse protein-protein interaction networks

  gene interacting interaction annotation yeast co-immunoprecipitation

• A comprehensive modular map of molecular interactions in RB/E2F pathway.

  PMID 18319725 · PMC2290939 · Molecular systems biology · 2008 · 8 claims · 4 setups

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  A comprehensive, curated map of RB/E2F pathway molecular interactions was built using SBGN notation in CellDesigner and converted to BioPAX 2.0 format

  bladder computational curated literature transcriptome gene network

• Indirect genomic effects on survival from gene expression data.

  PMID 18358079 · PMC2397510 · Genome biology · 2008 · 7 claims · 6 setups

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  A novel methodology (dynamic path analysis combined with additive hazard survival regression) can detect and quantify indirect effects of gene expression on survival mediated through transcription factor target genes.

  dutch uppsala dlbcl gene gene literature permutation

• Artificial Intelligence Approach in Machine Learning-Based Modeling and Networking of the Coronavirus Pathogenesis Pathway.

  PMID 40699865 · PMC12191508 · Current issues in molecular biology · 2025 · 8 claims · 8 setups

  Has reproduction

  The coronavirus pathogenesis pathway is activated in SARS-CoV-2-infected iPSC-derived cardiac cells and in SARS-CoV/SARS-CoV-2-infected LUAD cells

  ace2-transfected calu-3 luad ipsc-derived ipa sequencing convolutional