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PIMKL: Pathway Induced Multiple Kernel Learning
Manica Matteo; Cadow Joris; Mathis Roland; Rodriguez Martinez Maria;
Manica Matteo; Cadow Joris; Mathis Roland; Rodriguez Martinez Maria;
PIMKL: Pathway Induced Multiple Kernel Learning
Abstract
What • Accurate phenotype classification • Biological interpretability • Robustness to noise • Handling curse of dimensionality How Exploit prior knowledge from biological networks • Apply multiple kernel learning for feature encoding • Use pathway annotations to enable interpretability
Keywords
multiple kernel learning
multiple kernel learning
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- 2019IsAmongTopNSimilarDocuments
- 2019IsVersionOf
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average visibility views 141 download downloads 79 citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average Powered byBIP!
- 141views79downloads

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popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
influence
Influence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
impulse
Impulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
views
Views provided by UsageCounts

downloads
Downloads provided by UsageCounts

Average
Average
Average
141
79
Beta
Funded by
EC| PrECISE
Project
PrECISE
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION
- Funder: European Commission (EC)
- Project Code: 668858
- Funding stream: H2020 | RIA
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What • Accurate phenotype classification • Biological interpretability • Robustness to noise • Handling curse of dimensionality How Exploit prior knowledge from biological networks • Apply multiple kernel learning for feature encoding • Use pathway annotations to enable interpretability
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