Ovary transcriptome profiling via application of artificial intelligence predicts egg quality in striped bass

ABSTRACT: We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders em

ORGANISM(S): Morone saxatilis

SUBMITTER: Marine Genomics Core 

PROVIDER: E-GEOD-42804 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress