Svensson, F;
Norinder, U;
(2019)
Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction.
Journal of Chemical Information and Modeling
, 59
(4)
pp. 1598-1604.
10.1021/acs.jcim.9b00027.
Preview |
Text
Svensson_manuscript_rev_2.pdf - Accepted Version Download (1MB) | Preview |
Abstract
Multi-task 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 endpoints 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 5-10 %). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced datasets.
Archive Staff Only
 |
View Item |
