Multitask modeling with confidence using matrix factorization and conformal prediction
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Ulf Norinder Fredrik SvenssonAbstract
Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach for large scale bioactivity prediction. This approach handles both high degrees of missing data and label imbalances while still producing high quality predictive models. When applied to ten assay end points from PubChem, the models generated valid models with an efficiency of 74.0-80.1% at the 80% confidence level with similar performance both for the minority and majority class. Also when deleting progressively larger portions of the available data (0-80%) the performance of the models remained robust with only minor deterioration (reduction in efficiency between 5 and 10%). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced data sets.
Journal details
Volume 59
Issue number 4
Pages 1598-1604
Available online
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Full text links
Publisher website (DOI) 10.1021/acs.jcim.9b00027
Europe PubMed Central 30908915
Pubmed 30908915
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