eprintid: 25605
rev_number: 20
eprint_status: archive
userid: 4094
dir: disk0/00/02/56/05
datestamp: 2018-11-15 10:55:53
lastmod: 2019-11-21 15:01:53
status_changed: 2018-11-15 10:55:53
type: doctoralThesis
metadata_visibility: show
creators_name: YANG, MI
title: Applying machine learning to derive actionable insights in precision oncology
subjects: ddc-500
subjects: ddc-570
subjects: ddc-610
divisions: i-110400
divisions: i-140001
divisions: i-160100
divisions: i-911800
adv_faculty: af-14
keywords: bioinformatics, computational biology, machine learning
cterms_swd: 500
cterms_swd: 570
cterms_swd: 610
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.
date: 2019
id_scheme: DOI
id_number: 10.11588/heidok.00025605
ppn_swb: 1682109720
own_urn: urn:nbn:de:bsz:16-heidok-256055
date_accepted: 2018-11-12
advisor: HASH(0x55fc36c263c0)
language: eng
bibsort: YANGMIAPPLYINGMA2019
full_text_status: public
place_of_pub: Heidelberg, Germany
citation:   YANG, MI  (2019) Applying machine learning to derive actionable insights in precision oncology.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/25605/1/HEIDELBERG_Thesis_MIYANG_2018.pdf