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dc.contributor.author
Manica, Matteo
dc.contributor.supervisor
Aebersold, Rudolf
dc.contributor.supervisor
Rodríguez, María
dc.contributor.supervisor
Claassen, Manfred
dc.contributor.supervisor
Wild, Peter
dc.date.accessioned
2019-01-17T10:25:52Z
dc.date.available
2019-01-17T09:29:52Z
dc.date.available
2019-01-17T10:25:52Z
dc.date.issued
2018
dc.identifier.uri
http://hdl.handle.net/20.500.11850/316631
dc.identifier.doi
10.3929/ethz-b-000316631
dc.description.abstract
Recent years have testified unprecedented advances in the field of molecular systems biology. The increasing amount of data produced from disparate sources is giving us the possibility to study biological systems from a wide variety of angles at an incredibly fine scale. In this context keeping up with the pace of data production and fully exploiting the availability of information from multiple modalities is fundamental. In this work, methods to extract information from different data types are investigated and developed in order to improve complex diseases understanding and develop explainable personalized models for patient stratification. The different data modalities considered ranging from molecular data, such as genomics, transcriptomics, and proteomics to data from the literature, either in form of natural language from publications or structured data from databases. The common denominator to handle these different modalities is machine learning based on graph topologies summarizing molecular interactions that provide a high-level representation of the molecular processes governing cells behavior. The main focus is the study of these molecular interaction networks and their potential applications to personalized medicine. The thesis is structured around three main pillars: network reconstruction, integration of network information in interpretable machine learning algorithms and the development of personalized models. Network reconstruction is analyzed on two data modalities: on molecular data by implementing state-of-the-art inference models and investigating consensus strategies to ensure robust prediction and on natural language proposing a novel deep learning-based methodology. Integration of network information into machine learning models is tackled by making use of a multiple kernel learning algorithm that exploits pathway-induced kernels, a concept introduced in this work. Going towards patient personalized models, networks are also exploited in a dynamic perspective to perform large-scale logical modeling through the implementation of a framework for accelerated attractor analysis. Model personalization is also tackled at genomic level by considering two data modalities: copy number alterations and somatic mutations. These data are used to feed a novel inference algorithm, implemented during this work, to estimate patient-specific phylogenetic trees.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Exploring Multi-Modal Learning Approaches Towards Precision Medicine
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.size
196 p.
en_US
ethz.identifier.diss
25633
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03663 - Aebersold, Rudolf / Aebersold, Rudolf
en_US
ethz.date.deposited
2019-01-17T09:29:58Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-17T10:26:08Z
ethz.rosetta.lastUpdated
2019-01-17T10:26:08Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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