The gram-negative bacteria such as E. coli are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in the dairy cattle industry in order to prevent transmission of infectious agents to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies and accumulation of various kinds of ‘-omics’ data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze the related resources to improve the diagnosis and treatment of mastitis diseases and to provide mechanistic insights into the host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. This meta-analysis not only reinforced the key findings in individual studies but also enriched several novel terms. In the current research, twelve meta-genes were detected by the majority of Attribute Weighting algorithm (AW)s as the most informative genes. The Decision Tree model (DT)s efficiently discovered the best combination of the meta-genes as bio-signature and confirmed some of the top-ranked genes as biomarkers in dairy cows with E. coli mastitis. At the end of the work and by gene regulatory networks we were able to introduce the most important gene regulators and gene targets in response to E. coli mastitis. Keywords: Clinical Mastitis, Dairy Cattle, Attribute weighting algorithm, Decision tree models, Biomarker, Transcriptom.
