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).

• Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.

  PMID 34575658 · PMC8472680 · Journal of personalized medicine · 2021 · 6 claims · 4 setups

  Has reproduction · 86

  A training gene set of 719 unique upregulated DEGs (subtype-specific) can be used to build ML models that classify TNBC into BLIA, BLIS, MES, and LAR subtypes.

  known tnbc unique unknown gene machine term

• Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.

  PMID 19801557 · PMC2780952 · DNA research : an international journal for rapid publication of reports on genes and genomes · 2009 · 6 claims · 3 setups

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  An SVM trained on 69 binary features of known PID genes can accurately classify PID vs non-PID genes and predict novel candidate PID genes

  genome training svm support leave-one-out sensitivity

• All systems GO for understanding mouse gene function.

  PMID 15610553 · PMC549721 · Journal of biology · 2004 · 7 claims · 4 setups

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  Quantitative, multivariate cross-tissue expression measurements are powerfully predictive of gene function

  diverse tissues computational dye-swap reverse-transcription-coupled support

• 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

• Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.

  PMID 18560577 · PMC2409962 · PloS one · 2008 · 7 claims · 5 setups

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  Ranked Coexpression Groups (RCG) built from k=6 nearest coexpressed genes, combined with a majority-rule functional characterization, integrate multiple datasets/coexpression measures to generate high-confidence functional annotation predictions

  normal genome-wide same omim microarray sage computational