2018
DOI: 10.1101/243568
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Identifying biomarkers of anti-cancer drug synergy using multi-task learning

Abstract: Combining anti-cancer drugs has the potential to increase treatment efficacy. Because patient responses to drug combinations are highly variable, predictive biomarkers of synergy are required to identify which patients are likely to benefit from a drug combination. To aid biomarker identification, the DREAM challenge consortium has recently released data from a screen containing 85 cell lines and 167 drug combinations. The main challenge of these data is the low sample size: per drug combination, a median of 1… Show more

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Cited by 2 publications
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“…These strong AUPRCs demonstrate a fair trade-off between false positives and false negatives within our data ( Supp Fig 8 ). This is one of the highest reported accuracy measures for drug synergy prediction models based on any of these combination efficacy metrics 10, 29 .…”
Section: Resultsmentioning
confidence: 91%
“…These strong AUPRCs demonstrate a fair trade-off between false positives and false negatives within our data ( Supp Fig 8 ). This is one of the highest reported accuracy measures for drug synergy prediction models based on any of these combination efficacy metrics 10, 29 .…”
Section: Resultsmentioning
confidence: 91%