Cargando…
A hierarchical anatomical classification schema for prediction of phenotypic side effects
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832387/ https://www.ncbi.nlm.nih.gov/pubmed/29494708 http://dx.doi.org/10.1371/journal.pone.0193959 |
_version_ | 1783303311631319040 |
---|---|
author | Wadhwa, Somin Gupta, Aishwarya Dokania, Shubham Kanji, Rakesh Bagler, Ganesh |
author_facet | Wadhwa, Somin Gupta, Aishwarya Dokania, Shubham Kanji, Rakesh Bagler, Ganesh |
author_sort | Wadhwa, Somin |
collection | PubMed |
description | 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, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects. |
format | Online Article Text |
id | pubmed-5832387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58323872018-03-23 A hierarchical anatomical classification schema for prediction of phenotypic side effects Wadhwa, Somin Gupta, Aishwarya Dokania, Shubham Kanji, Rakesh Bagler, Ganesh PLoS One Research Article 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, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects. Public Library of Science 2018-03-01 /pmc/articles/PMC5832387/ /pubmed/29494708 http://dx.doi.org/10.1371/journal.pone.0193959 Text en © 2018 Wadhwa et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wadhwa, Somin Gupta, Aishwarya Dokania, Shubham Kanji, Rakesh Bagler, Ganesh A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title | A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title_full | A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title_fullStr | A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title_full_unstemmed | A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title_short | A hierarchical anatomical classification schema for prediction of phenotypic side effects |
title_sort | hierarchical anatomical classification schema for prediction of phenotypic side effects |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832387/ https://www.ncbi.nlm.nih.gov/pubmed/29494708 http://dx.doi.org/10.1371/journal.pone.0193959 |
work_keys_str_mv | AT wadhwasomin ahierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT guptaaishwarya ahierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT dokaniashubham ahierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT kanjirakesh ahierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT baglerganesh ahierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT wadhwasomin hierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT guptaaishwarya hierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT dokaniashubham hierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT kanjirakesh hierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects AT baglerganesh hierarchicalanatomicalclassificationschemaforpredictionofphenotypicsideeffects |