Drug side-effect prediction using machine learning methods

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Perustieteiden korkeakoulu | Master's thesis
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Date

2017-12-11

Department

Major/Subject

Bioinformatics

Mcode

SCI3058

Degree programme

Master's Degree Programme in Computational and Systems Biology (euSYSBIO)

Language

en

Pages

52 + 1

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Abstract

Drug toxicity (or adverse side effects) is a pressing health problem which is also an impediment to the development of therapeutically effective drugs. Despite many on-going efforts to determine the toxicity beforehand, computational prediction of drug side-effects remains a challenging task. This thesis presents an approach to predict side-effects by utilizing side-information sources for the drugs, while simultaneously comparing state-of-the-art machine learning methods to improve accuracy. Specifically, the thesis implements a data-analysis pipeline for obtaining side-information that are useful for the prediction task. This thesis then formulates the drug side-effect prediction as a machine learning problem: Given disease indications and structural features (as side-information sources) of drugs, for which some measurements of side-effect exist, predict sideeffect for a new drug. As case studies, the prediction accuracies are compared for ten different side-effects using linear as well as non-linear machine learning methods. The thesis summarizes three key findings. First, the drug side-information sources are predictive of the side-effects. Second, non-linear methods show improved prediction accuracies as compared to their linear analogs. Third, the integration of disease indications and structural features with a principled machine learning approach further improves the drug side-effect predictions. However, the current study limits the analysis assuming side-effects are independent. In future, modeling the joint relationships of several side-effects could yield more strong predictions and better help to understand the underlying biological mechanism.

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Supervisor

Kaski, Samuel

Thesis advisor

Martinen, Pekka
Tang, Jing

Keywords

drug side-effects, machine learning, drug side-effect prediction, comparison of machine learning methods

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https://urn.fi/URN:NBN:fi:aalto-201712188103

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[dipl] Perustieteiden korkeakoulu / SCI