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Applying machine learning to derive actionable insights in precision oncology


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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.

Item Type: Dissertation
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 > Department of Applied Mathematics
The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
The Faculty of Bio Sciences > Institute of Pharmacy and Molecular Biotechnology
Medizinische Fakultät Heidelberg > Institut für Medizinische Biometrie und Informatik
Subjects: 500 Natural sciences and mathematics
570 Life sciences
610 Medical sciences Medicine
Controlled Keywords: 500, 570, 610
Uncontrolled Keywords: bioinformatics, computational biology, machine learning
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