Supplementary Table S2 from Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Parameter estimates (credible interval) of affinities of Ab

Supplementary Table S2 from Bayesian inference of antigenic and non-antigenic variables from haemagglutinin inhibition assays for influenza surveillance

Parameter estimates (credible interval) of affinities of Ab

Supplementary Table S3 from Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Parameter estimates (credible interval) of non-antigenic variable

Supplementary Table S4 from Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

A collection of HI titer

Supplementary Table S1 from Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Parameter estimates (credible intervals) of concentrations of Ab

Supplementary Material from Bayesian inference of antigenic and non-antigenic variables from haemagglutinin inhibition assays for influenza surveillance

Additional methods and result

Supplementary Material from Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Additional methods and result

Supplementary Table S4 from Bayesian inference of antigenic and non-antigenic variables from haemagglutinin inhibition assays for influenza surveillance

A collection of HI titer

Supplementary Table S1 from Bayesian inference of antigenic and non-antigenic variables from haemagglutinin inhibition assays for influenza surveillance

Parameter estimates (credible intervals) of concentrations of Ab

SAGA: A hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks

AbstractBayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks