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-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network.

  PMID 19728866 · PMC2768980 · Genome biology · 2009 · 6 claims · 6 setups

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  Integrating 16 genomic features (32 sub-features) via a naïve Bayes classifier produces a genome-scale FLN of 21,657 human genes and 22,388,609 weighted links that outperforms any individual data source for inferring functional linkages.

  genome gene omim known naïve linkage disease-centric

• Bayesian prediction of microbial oxygen requirement.

  PMID 26913185 · PMC4743139 · F1000Research · 2013 · 7 claims · 8 setups

  Has reproduction · 76

  A naive Bayesian classifier based on presence/absence of class-associated Pfam-A domains can distinguish three oxygen requirement classes (aerobe, anaerobe, facultative anaerobe) from genome sequence, unlike prior studies that only made pairwise distinctions.

  bacterial selected predicted pfam-a genome gene domain

• Genomic and transcriptomic plasticity in treatment-naive ovarian cancer.

  PMID 24221193 · PMC3912411 · Genome research · 2014 · 8 claims · 8 setups

  Has reproduction · 30

  Treatment-naïve epithelial ovarian cancers show extensive intra-tumor heterogeneity of genomic rearrangements, with the most substantial differences occurring between omentum/peritoneum metastases and ovarian tumor sites.

  tumor each rna-seq whole-genome targeted pcr-based

• Prioritization of candidate cancer genes--an aid to oncogenomic studies.

  PMID 18710882 · PMC2566894 · Nucleic acids research · 2008 · 8 claims · 8 setups

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  Computational classifiers using combinations of protein conservation, gene structure, protein domains, protein interactions, and regulatory data can distinguish known cancer genes (CD/CR) from unlabelled human genes

  genes cancer interpro interactome averaged cross-validation domain

• Integrative network modeling reveals mechanisms underlying T cell exhaustion.

  PMID 32024856 · PMC7002445 · Scientific reports · 2020 · 8 claims · 6 setups

  Has reproduction · 59

  An integrative, literature-curated and data-driven gene regulatory network underlies CD8+ T cell exhaustion and accurately captures expression states in chronic infection and tumor settings.

  cd8+ tce activated manual gene principal

• Filtering high-throughput protein-protein interaction data using a combination of genomic features.

  PMID 15833142 · PMC1127019 · BMC bioinformatics · 2005 · 8 claims · 8 setups

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  A combination of three genomic features (interacting Pfam domains, GO annotations, sequence homology) using naive Bayesian networks predicts true protein-protein interactions with high sensitivity and good specificity.

  saccharomyces bank caenorhabditis homo yeast mass naive