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Conference Paper: MultiFacTV: Finding modules from higher-order gene expression profiles with time dimension

TitleMultiFacTV: Finding modules from higher-order gene expression profiles with time dimension
Authors
Keywordstensor factorization
total variation
module detection
regularization
alternating directions method
Issue Date2012
Citation
Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012, 2012, p. 53-58 How to Cite?
AbstractModule detection is an important task in bioinformatics which aims at finding a set of cells/genes that interact together to be responsible for some biological functionalities. In this paper, we propose a novel tensor factorization approach to finding modules from higher-order gene expression profiles with the time dimension, e.g., gene x condition x time data. The main idea is to incorporate a total variation regularization term for the time dimension during the tensor factorization, and then use the factorization results to identify the modules. Experimental results on two real gene x condition x time datasets have shown the effectiveness of the proposed method. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/276946

 

DC FieldValueLanguage
dc.contributor.authorLi, Xutao-
dc.contributor.authorYe, Yunming-
dc.contributor.authorWu, Qingyao-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:35:08Z-
dc.date.available2019-09-18T08:35:08Z-
dc.date.issued2012-
dc.identifier.citationProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012, 2012, p. 53-58-
dc.identifier.urihttp://hdl.handle.net/10722/276946-
dc.description.abstractModule detection is an important task in bioinformatics which aims at finding a set of cells/genes that interact together to be responsible for some biological functionalities. In this paper, we propose a novel tensor factorization approach to finding modules from higher-order gene expression profiles with the time dimension, e.g., gene x condition x time data. The main idea is to incorporate a total variation regularization term for the time dimension during the tensor factorization, and then use the factorization results to identify the modules. Experimental results on two real gene x condition x time datasets have shown the effectiveness of the proposed method. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012-
dc.subjecttensor factorization-
dc.subjecttotal variation-
dc.subjectmodule detection-
dc.subjectregularization-
dc.subjectalternating directions method-
dc.titleMultiFacTV: Finding modules from higher-order gene expression profiles with time dimension-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/BIBM.2012.6392641-
dc.identifier.scopuseid_2-s2.0-84872549196-
dc.identifier.spage53-
dc.identifier.epage58-

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