Comparing the reconstruction of regulatory pathways with distinct Bayesian networks inference methods

Submitted by Taís Renata Pereira Amorim (tatahamorim_sjn@hotmail.com) on 2014-11-20T17:26:21Z No. of bitstreams: 1 Comparing the reconstruction of regulatory.pdf: 460401 bytes, checksum: 31c7351563b5a4597d15625688cda51d (MD5)Approved for entry into archive by Paula Gautério (paulaporto_g@hotmail.com) on 2014-12-04T23:06:42Z (GMT) No. of bitstreams: 1 Comparing the reconstruction of regulatory.pdf: 460401 bytes, checksum: 31c7351563b5a4597d15625688cda51d (MD5)Made available in DSpace on 2014-12-04T23:06:42Z (GMT). No. of bitstreams: 1 Comparing the reconstruction of regulatory.pdf: 460401 bytes, checksum: 31c7351563b5a4597d15625688cda51d (MD5) Previous issue date: 2012Background: Inference of biological networks has become an important tool in Systems Biology. Nowadays it is becoming clearer that the complexity of organisms is more related with the organization of its components in networks rather than with the individual behaviour of the components. Among various approaches for inferring networks, Bayesian Networks are very attractive due to their probabilistic nature and flexibility to incorporate interventions and extra sources of information. Recently various attempts to infer networks with different Bayesian Networks approaches were pursued. The specific interest in this paper is to compare the performance of three different inference approaches: Bayesian Networks without any modification; Bayesian Networks modified to take into account specific interventions produced during data collection; and a probabilistic hierarchical model that allows the inclusion of extra knowledge in the inference of Bayesian Networks. The inference is performed in three different types of data: (i) synthetic data obtained from a Gaussian distribution, (ii) synthetic data simulated with Netbuilder and (iii) Real data obtained in flow cytometry experiments. Results: Bayesian Networks with interventions and Bayesian Networks with inclusion of extra knowledge outperform simple Bayesian Networks in all data sets when considering the reconstruction accuracy and taking the edge directions into account. In the Real data the increase in accuracy is also observed when not taking the edge directions into account. Conclusions: Although it comes with a small extra computational cost the use of more refined Bayesian network models is justified. Both the inclusion of extra knowledge and the use of interventions have outperformed the simple Bayesian network model in simulated and Real data sets. Also, if the source of extra knowledge used in the inference is not reliable the inferred network is not deteriorated. If the extra knowledge has a good agreement with the data there is no significant difference in using the Bayesian networks with interventions or Bayesian networks with the extra knowledge