Prior knowledge driven causality analysis in gene regulatory network discovery

Shun Yao, Shinjae Yoo, Dantong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Previous researches focus on applying the Granger causality (GC) model to time-series DNA micro array data to infer gene regulatory networks. However, in biological datasets, the number of available time points is usually much smaller than the number of target genes. Therefore, people widely used a bivariate GC model, which might lead to a significant amount of false discoveries in the causality analysis. In this study, we proposed a new framework to resolve the problem by incorporating the prior biological knowledge. These prior knowledge helps us to use/build a gene association network and cluster the candidate gene set into smaller groups. Within each small group, the more precise multivariate GC model is applied to discover causalities. We validated this new framework to a yeast metabolic cycle dataset and initial analysis revealed the potentials of our approach in discovering meaningful regulatory networks.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013
PublisherIEEE Computer Society
Pages124-130
Number of pages7
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
Event2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013

Other

Other2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
CountryUnited States
CityDallas, TX
Period12/7/1312/10/13

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Yao, S., Yoo, S., & Yu, D. (2013). Prior knowledge driven causality analysis in gene regulatory network discovery. In Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 (pp. 124-130). [6753911] IEEE Computer Society. https://doi.org/10.1109/ICDMW.2013.107