Semantic Biclustering: A New Way to Analyze and Interpret Gene Expression Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00300018" target="_blank" >RIV/68407700:21230/16:00300018 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-38782-6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-38782-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-38782-6" target="_blank" >10.1007/978-3-319-38782-6</a>

Result language
angličtina
Original language name
Semantic Biclustering: A New Way to Analyze and Interpret Gene Expression Data
Original language description
We motivate and define the task of semantic biclustering. In an input gene expression matrix, the task is to discover homogeneous biclusters allowing joint characterization of the contained elements in terms of knowledge pertaining to both the rows (e.g. genes) and the columns (e.g. situations). We propose two approaches to solve the task, based on adaptations of current biclustering, enrichment, and rule and tree learning methods. We compare the approaches in experiments with Drosophila ovary gene expression data. Our findings indicate that both the proposed methods induce compact bicluster sets whose description is applicable to unseen data. The bicluster enrichment method achieves the best performance in terms of the area under the ROC curve, at the price of employing a large number of ontology terms to describe the biclusters.
Czech name
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Czech description
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Project
<a href="/en/project/GA14-21421S" target="_blank" >GA14-21421S: Automatic analysis of spatial gene expression patterns</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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