Prediction of Liver Toxicity using Machine Learning to aid Drug Discovery
Ladda ner
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis proposes a method for predicting drug incuded liver injury using transcriptomic
data from the toxicogenomical databases TG-GATEs (Toxicogenomics
Project-Genomics Assisted Toxicity Evaluation System) and DrugMatrix with the
help of various machine learning algorithms. The possibility of using the toxicological
database CMap in cooperation with the NCI60 human tumor cell lines screen
to make prediction models for in vitro cytotoxicity using the same methodology was
also investigated.
It was found that transcriptomic data can indeed be used to predict liver injury
in rat with very high accuracy. The in silico models developed in this project also
outperform similar existing solutions on completely external testing sets, generating
models successfully predicting four different injuries in the context of liver: necrosis,
fibrosis, hyperplasia and mitotic alterations.
In vitro cytotoxicity was also predicted by the models with relatively high accuracy,
more specifically on the cancer cell line A-549. The model was also evaluated
on primary human hepatocytes exposed to hepatotoxic agents, finding dose-response
relationships. Additional learnings included the importance of selecting appropriate
featuresets when predicting specific adverse effects and also the applicability of synthetic
oversampling techniques in collaboration with transfer-learning when used on
transcriptomic data.
Beskrivning
Ämne/nyckelord
Computer, science, computer science, engineering, thesis, data science, dili, deep learning, machine learning, cytotoxicity, connectivity map, tg-gates, drugmatrix, nci60, biotechnology, transcriptomics, bioinformatics