A dynamic Bayesian framwork to learn temporal gene interactions using external knowledge
Küçük Resim Yok
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
One of the main problems in systems biology is learning gene interaction networks from experimental data. This turns out to be a challenging task as the experimental data is sparse and noisy, and network learning algorithms are computationally intense. Bayesian Networks (BN) have become a popular choice for learning such networks as BNs avoid overfitting and are robust to noise. In this paper we build up on our established framework, Bayesian Network Prior, where we incorporate existing biological knowledge in learning gene interaction networks. However, biological phenomena are time-dependent and there is need to extend the static structure of learning approaches to a temporal level. Here, we present a Dynamic BN framework, which learns interaction networks between different time points in time-series data. Both intra and inter networks are learnt and compared to standard DBN learning algorithms. Our results based on synthetic and simulated gene expression data suggest that the proposed method outperforms existing approaches in identifying the underlying network structure. The proposed framework is robust to errors in the incorporated knowledge and can combine various experimental data types together with existing knowledge when learning networks. © 2013 IEEE.
Açıklama
BSN Anatolia;European Commission;Fulbright;ODTU Teknokent
2013 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013 -- 25 September 2013 through 27 September 2013 -- Ankara -- 101984
2013 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013 -- 25 September 2013 through 27 September 2013 -- Ankara -- 101984
Anahtar Kelimeler
Dynamic Bayesian Networks, External Biological Knowledge, Gene İnteraction Networks, Microarray, Time-Series Data, Active Networks, Bioinformatics, Gene Expression, Learning Algorithms, Microarrays, Biological Phenomena, Dynamic Bayesian Networks, External Biological Knowledge, Gene Expression Data, Gene İnteraction Networks, Interaction Networks, Network Learning Algorithms, Time-Series Data, Bayesian Networks
Kaynak
2013 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013
WoS Q Değeri
Scopus Q Değeri
N/A
