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

Xutao Li*, Yunming Ye, Qingyao Wu, Kwok Po NG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Module 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.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
Pages53-58
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012 - Philadelphia, PA, United States
Duration: 4 Oct 20127 Oct 2012

Publication series

NameProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012

Conference

Conference2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012
Country/TerritoryUnited States
CityPhiladelphia, PA
Period4/10/127/10/12

Scopus Subject Areas

  • Biomedical Engineering
  • Health Informatics

User-Defined Keywords

  • alternating directions method
  • module detection
  • regularization
  • tensor factorization
  • total variation

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