TY  -  
T1  - Application of Machine Learning methods to identify important biomarkers from untargeted metabolomics data 
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UR  - URN:NBN:fi:hulib-202104121870; http://hdl.handle.net/10138/328939 
T3  -  
A1  - Hämäläinen, Kreetta 
A2  -  
PB  - Helsingin yliopisto 
Y1  - 2021 
LA  - eng 
AB  - Personalized medicine tailors therapies for the patient based on predicted risk factors. Some tools used for making predictions on the safety and efficacy of drugs are genetics and metabolomics. This thesis focuses on identifying biomarkers for the activity level of the drug transporter organic anion transporting polypep-tide 1B1 (OATP1B1) from data acquired from untargeted metabolite profiling. OATP1B1 transports various drugs, such as statins, from portal blood into the hepatocytes. OATP1B1 is... 
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KW  - metabolomics; OATP1B1; machine learning; neural networks; random forests; decision trees; Algoritminen bioinformatiikka; Algorithmic Bioinformatics; Algoritmisk bioinformatik; Life Science Informatics -maisteriohjelma; Master's Programme in Life Science Informatics; Magisterprogrammet i Life Science Informatics 
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