Sentence examples for terms we performed from inspiring English sources

Using equivalent terms, we performed additional searches for publications in the Chinese medical literature using the Wanfang (http://www.wanfangdata.com/) and Chongqingvip (www.cqvip.com) databases [10], [11].

To determine significantly enriched GO terms, we performed enrichment analysis using the TopGO R package (Alexa et al., 2006) on each individual's differentially expressed genes at training time.

For each GO term, we performed a chi squared test of the null hypothesis that the proportion of gene duplicates associated with that term was the same for both pathogens and non-pathogens [ 82, 96].

For each GO Slim term, we performed a Fisher's exact test and then applied a cutoff p-value of 0.05 to identify GO Slim terms enriched in a set of target genes.

To determine the significance of the interaction term, we performed a likelihood ratio test (LRT) between the full (Y = β0 + β1SNP1 + β2SNP2 + β3SNP1×SNP2) and reduced (Y = β0 + β1SNP1 + β2SNP2) models.

For each Gene Ontology term we perform a hypergeometric test (one-sided Fisher exact test) for edges (gene pairs).

Then, for each GO term, we perform Spearman's rank correlation test [ 43] for the ranks of the betweenness centrality values.

Then, for each GO term, we perform a Spearman's rank correlation test [ 38] for the ranks of the values for each centrality measure between a pair of networks.

For each Gene Ontology term we perform a hypergeometric test (one-sided Fisher exact test) for the enrichment of gene pairs sharing the same functional annotation between two networks (analog to eqn. 8).

Multivariate Cox proportional hazards regression models were fitted by using all variables included as main effects terms, and we performed corresponding linear trend tests.

For both scenarios, we fitted models that differ in the infection term G and the tumor growth term F. We performed non-linear least squares regression, using standard software.