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

• Ab initio identification of human microRNAs based on structure motifs.

  PMID 18088431 · PMC2238772 · BMC bioinformatics · 2007 · 8 claims · 7 setups

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  MiRPred predicts miRNA precursors ab initio using only predicted secondary structure motifs, ignoring nucleotide sequence

  genome mirna hairpins chromosome secondary linear five-fold

• Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing.

  PMID 31443728 · PMC6708158 · Genome medicine · 2019 · 8 claims · 6 setups

  Has reproduction · 68

  The proportion of variance in chronological age explained by all DNA methylation probes is close to 1, so a near-perfect age predictor is in principle achievable with sufficient training data.

  blood unrelated training lbc1921 methylation reml age

• Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.

  PMID 31918046 · PMC6992905 · iScience · 2020 · 8 claims · 8 setups

  Has reproduction · 96

  Data-driven, high-dimensional ML approaches that learn multivariate signatures directly from genome-wide transcriptomic data (no prior gene selection) yield accurate and robust AML classifiers.

  peripheral pbmc datasets hg-u133 gene bulk machine

• Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

  PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups

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  CSVME, built by selecting a subset of base SVMs via SVM-RFE ranking and fusing them with a trained upper-layer SVM, achieves better classification performance than an ensemble of all base SVMs.

  serum computational seldi-tof svm-rfe-based 10-fold

• Predicting failure rate of PCR in large genomes.

  PMID 18492719 · PMC2441781 · Nucleic acids research · 2008 · 7 claims · 8 setups

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  The number of predicted primer-binding sites in genomic DNA is the most important factor determining PCR failure.

  genomic genome pcr combined large-scale exact-match mismatch-tolerant