All functions |
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applyBenjaminiHochberg |
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buildLRModel() |
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buildRecipe() |
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buildTuningGrid() |
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buildWflow() |
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calculateEvalMets() |
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calculateMinSamples() |
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computeFeatureFreq |
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Create machine learning result directories |
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Create machine learning input list |
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.calculateAUPRC() |
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.calculateBalAcc() |
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.calculateF1() |
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.calculateLog2APOP() |
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.calculateSensitivity() |
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.calculateSpecificity() |
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.calculatenMCC() |
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.getFitHps() |
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.getTargetVarName() |
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Build leave-one-out (LOO) merged parquet matrices from stratified parquet files. |
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encodePhenotype |
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extractTopFeats() |
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fitBestModel() |
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Generate all ML input matrices |
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getConfusionMatrix() |
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getNumFeat() |
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loadMLInputTibble() |
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plotBaselineComparison() |
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plotDefaultEval() |
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Plot top features' Fisher's significance |
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plotPRC() |
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plotTopFeatsVI() |
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predictML() |
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removeTopFeats |
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runFisherTests |
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runFishers |
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runIFE Removes top features identified by ML models and retrains iteratively; returns nMCC at each iteration. |
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Run MDR (multi-drug resistance) machine learning models |
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runMLPipeline() |
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Run machine learning models with multiple configurations |
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Run the entire AMR ML pipeline from a parquet-backed DuckDB |
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selectBestModel() |
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shuffleLabels() |
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splitMLInputTibble() |
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tuneGrid() |
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