Many computational methods have been recently proposed to identify differentially abundant microbes related to a single disease; however, few studies have focused on large-scale microbe-disease association prediction using existing experimentally verified associations. This area has critical meanings. For example, it can help to rank and select potential candidate microbes for different diseases at-scale for downstream lab validation experiments and it utilizes existing evidence instead of the microbiome abundance data which usually costs money and time to generate.

中文翻译：

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NinimHMDA：多重异构网络上邻域信息的神经整合，用于多种类型的人类微生物-疾病关联

最近提出了许多计算方法来识别与单一疾病相关的差异丰富的微生物；然而，很少有研究关注使用现有实验验证的关联进行大规模微生物-疾病关联预测。该区域具有重要意义。例如，它可以帮助对不同疾病的潜在候选微生物进行大规模排序和选择，以进行下游实验室验证实验，并且它利用现有证据而不是通常需要花费金钱和时间来生成的微生物组丰度数据。