Additional file 3 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
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posted on 2024-08-15, 06:15 authored by Juman Jubran, Rachel Slutsky, Nir Rozenblum, Lior Rokach, Uri Ben-David, Esti Yeger-LotemAdditional file 3: Table S2. The auROC and auPRC performance of ML models whose features were calculated using distinct percentages of genes.
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Israel Science Foundation Ben-Gurion University Grant European Research Council Starting Grant BSF Project Grant Israel Cancer Research Fund European Molecular Biology Organization
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one paralog interactionlearning analysis revealsdefine 20 typesconfirming known drivers24 cancer typesquantitative analysis highlightscancer cell linesshaping aneuploidy landscapesselective aneuploidy patternsmachine learning modelsaneuploidy landscapesselective featuresaneuploidy researchcolon cancercancer developmentvice versatop predictorsrevealing klf5relative contributionprimary tumorspositive selectiononcogene densitynormal tissuesnegative selectionmodel gainsloss patternsimportant roleimportant driverhuman cclsgenomic propertiesgenomic attributesgain patternsfully understoodfind compensationfeatures correspondingchromosomes withinchr13q gainccls ),better predictoralso identifyabnormal number
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