Skip to Main content Skip to Navigation

Personalized drug adverse side effect prediction

Abstract : Adverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications.
Complete list of metadata

Cited literature [129 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Tuesday, March 20, 2018 - 1:15:08 PM
Last modification on : Monday, June 27, 2022 - 3:03:45 AM
Long-term archiving on: : Tuesday, September 11, 2018 - 8:55:05 AM


Version validated by the jury (STAR)


  • HAL Id : tel-01738245, version 1


Víctor Bellón Molina. Personalized drug adverse side effect prediction. Medication. Université Paris sciences et lettres, 2017. English. ⟨NNT : 2017PSLEM023⟩. ⟨tel-01738245⟩



Record views


Files downloads