Show simple item record

dc.contributor.author
Manica, Matteo
dc.contributor.author
Cadow, Joris
dc.contributor.author
Mathis, Roland
dc.contributor.author
Rodriguez Martinez, María
dc.date.accessioned
2019-03-19T09:42:31Z
dc.date.available
2019-03-16T04:13:59Z
dc.date.available
2019-03-19T09:42:31Z
dc.date.issued
2019
dc.identifier.other
10.1038/s41540-019-0086-3
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/331766
dc.identifier.doi
10.3929/ethz-b-000331766
dc.description.abstract
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behavior might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels to predict a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
PIMKL: Pathway-Induced Multiple Kernel Learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-03-05
ethz.journal.title
npj Systems Biology and Applications
ethz.journal.volume
5
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
8
en_US
ethz.size
8 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2019-03-16T04:13:59Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-03-19T09:42:53Z
ethz.rosetta.lastUpdated
2020-02-15T17:54:30Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=PIMKL:%20Pathway-Induced%20Multiple%20Kernel%20Learning&rft.jtitle=npj%20Systems%20Biology%20and%20Applications&rft.date=2019&rft.volume=5&rft.issue=1&rft.spage=8&rft.au=Manica,%20Matteo&Cadow,%20Joris&Mathis,%20Roland&Rodriguez%20Martinez,%20Mar%C3%ADa&rft.genre=article&rft_id=info:doi/10.1038/s41540-019-0086-3&
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record