MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only.

Integrated analysis of lncRNAs and mRNAs reveals key trans-target genes associated with ETEC-F4ac adhesion phenotype in porcine small intestine epithelial cells.

Background: Long non-coding RNAs (lncRNAs) play crucial roles in gene regulation at the transcriptional and post-transcriptional levels. LncRNAs are belonging to a large class of transcripts with ≥200 nt in length which do not code for proteins, have been widely investigated in various physiological and pathological contexts by high-throughput sequencing techniques and bioinformatics analysis. However, little is known about the regulatory mechanisms by which lncRNAs regulate genes that are associated with Enterotoxigenic Escherichia coli F4 fimbriae (ETEC-F4ac) adhesion phenotype in small intestine epithelial cells of Large White piglets.