Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction

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.