首页|Characterizing gene expression profiles of various tissue states in stony coral tissue loss disease using a feature selection algorithm
Characterizing gene expression profiles of various tissue states in stony coral tissue loss disease using a feature selection algorithm
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - According to news reporting based on a preprint a bstract, our journalists obtained the followingquote sourced from biorxiv.org:“Stony coral tissue loss disease (SCTLD) remains a substantial threat to coral r eef diversity alreadythreatened by global climate change. Restoration efforts a nd effective treatment of SCTLD requiresan in-depth understanding of its pathog enesis in the coral holobiont as well as mechanisms of diseaseresistance. Here, we present a supervised machine learning framework to describe SCTLD progressio n ina major reef-building coral, Montastraea cavernosa, and its dominant algal endosymbiont, Cladocopiumgoreaui. Utilizing support vector machine recursive fe ature elimination (SVM-RFE) in conjunction withdifferential expression analysis , we identify a subset of biologically relevant genes that exhibit the highestc lassification performance across three types of coral tissues collected from a n atural reef environment:apparently healthy tissue on an apparently healthy colo ny, apparently healthy tissue on a SCTLD-affectedcolony, and lesion tissue on a SCTLD-affected colony. By analyzing gene expression signatures associatedwith these tissue health states in both the coral host and its algal endosymbiont (fa mily Symbiodiniaceae),we describe key processes involved in SCTLD resistance an d disease progression within the coral holobiont.
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