Sparse Omics-network Regularization to Increase Interpretability and Performance of Linear Classification Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00234068" target="_blank" >RIV/68407700:21230/15:00234068 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Sparse Omics-network Regularization to Increase Interpretability and Performance of Linear Classification Models
Original language description
Current high-throughput technologies lead to the boost of omics data with thousands of features measured in parallel. The phenotype specific markers are learned from the data to better understand the disease mechanism and to build predictive models. However, the learning is prone to overfitting, caused by a small sample size and large feature space dimension. Consequently, resulting models are inaccurate and difficult to interpret due to the complex nature of omics processes. In this paper, we propose a methodology for learning simple yet biologically meaningful linear classification models. A linear support vector machine is trained; the learning is regularized by prior knowledge. Regularization parameters enable the expert to operatively adjust the interpretation of the models and their conformity with recent domain research while maintaining their accuracy. We performed robust experiments showing empirical validity of our methodology. In the study related to myelodysplastic syndrome we demonstrate the performance and interpretation of disease classification models. These models are consistent with recent progress in myelodysplastic syndrome research.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2014 IEEE International Conference on Bioinformatics and Biomedicine
ISBN
978-1-4673-6798-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
615-620
Publisher name
IEEE
Place of publication
Piscataway (New Jersey)
Event location
Washington
Event date
Nov 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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