摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑新闻-根据基于预印本的新闻报道，我们的记者获得了以下信息报价来源：biorxiv.org：“石珊瑚组织丢失病(SCTLD)仍然是珊瑚多样性的重大威胁”受到全球气候变化的威胁。SCTLD的修复和有效治疗需要对珊瑚全缘生物的致病机理及发病机制的深入认识抵抗在此,我们提出了一个有监督的机器学习框架来描述SCTLD的进展一种主要造礁珊瑚，洞穴蒙塔斯特雷亚及其优势藻类内共生体枝藻高瑞欧结合支持向量机递归消去(SVM-RFE)差异表达分析，我们发现了一个生物学相关基因子集，表现出最高的c从自然珊瑚礁环境中采集的三种珊瑚组织的分类性能：表面上健康的组织在表面上健康的科罗拉多州，表面上健康的组织在SCTLD患者身上菌落，以及SCTLD影响菌落上的损伤组织。通过分析相关基因表达信号珊瑚宿主及其藻类内共生体(Fa mily Symbiodiniaceae)中的这些组织健康状态，我们描述了珊瑚全缘中涉及SCTLD抗性和疾病进展的关键过程。
Abstract
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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