Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle.

ABSTRACT: Gram-negative bacteria such as Escherichia coli (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 order to prevent transmission 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 these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of singl

SUBMITTER: Sharifi S 

PROVIDER: S-EPMC5823400 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature