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Abstract
Cancer drugs have among the lowest response rates across all diseases. Combining the wealth of omics data and machine learning is a promising way to reach this goal. In this thesis, we addressed the following aspects of precision oncology: (i) We used Macau, a bayesian multitask multi-relational algorithm to explore the associations between the drugs’ targets and signaling pathways’ activation. We applied this methodology to drug synergy prediction and stratification. (ii) We leveraged through a collaborative machine learning competition to understand the association between genome, transcriptome and proteome in tumors. The main focus of this thesis is to use machine learning to generate actionable insights, for more personalized therapies.
Document type: | Dissertation |
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Supervisor: | Saez-Rodriguez, Prof. Dr. Julio |
Place of Publication: | Heidelberg, Germany |
Date of thesis defense: | 12 November 2018 |
Date Deposited: | 15 Nov 2018 10:55 |
Date: | 2019 |
Faculties / Institutes: | The Faculty of Mathematics and Computer Science > Institut für Mathematik The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences Fakultät für Ingenieurwissenschaften > Institute of Pharmacy and Molecular Biotechnology Medizinische Fakultät Heidelberg > Institut für Medizinische Biometrie und Informatik |
DDC-classification: | 500 Natural sciences and mathematics 570 Life sciences 610 Medical sciences Medicine |
Controlled Keywords: | 500, 570, 610 |
Uncontrolled Keywords: | bioinformatics, computational biology, machine learning |