近期,来自复旦大学附属儿科医院王明帮、周文浩和德宏州人民医院Zhaoqing Yin与团队,在Computational and Structural Biotechnology Journal上发表的一项最新研究Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder,发现ASD患儿与正常儿童的毒力因子相关肠道菌群(VFGM)基因的组成及多样性有显著差异,结合VFGM基因多样性、肠道IgA水平及特定VFGM基因的丰度开发机器学习算法,可准确区分ASD患儿及正常儿童。