A hierarchical anatomical classification schema for prediction of phenotypic side effects

PloS One
Somin WadhwaGanesh Bagler

Abstract

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated...Continue Reading

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Citations

Mar 29, 2019·BMC Bioinformatics·İlknur Karadeniz, Arzucan Özgür
Mar 20, 2020·BioMed Research International·Sukyung SeoYoungmi Yoon

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Methods Mentioned

BETA
PCA

Software Mentioned

Discovery Studio
STITCH Search Tool for
Python
SIDER
D3

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