美国加州大学旧金山分校Rob Knight及其研究组报道了可用于分析微生物与代谢产物之间相互作用的人工神经网络。相关论文2019年11月4日在线发表于《自然—方法学》。

研究人员表示，整合多组学数据集对于微生物组研究至关重要。但是，推断整个组学数据集之间的交互具有多种统计学上的挑战。

研究人员通过使用神经网络（https://github.com/biocore/mmvec）来解决了此问题，其能够在存在特定微生物的情况下估算每个分子存在的条件概率。研究人员以已知的环境（沙漠土壤湿润生物壳）和临床（囊性纤维化肺）实例为例，展示了这一方法恢复微生物与代谢物之间关系的能力，并证明了该方法如何发现微生物产生的代谢产物与炎症性肠病之间的关系。

附：英文原文

Title: Learning representations of microbe–metabolite interactions

Author: James T. Morton, Alexander A. Aksenov, Louis Felix Nothias, James R. Foulds, Robert A. Quinn, Michelle H. Badri, Tami L. Swenson, Marc W. Van Goethem, Trent R. Northen, Yoshiki Vazquez-Baeza, Mingxun Wang, Nicholas A. Bokulich, Aaron Watters, Se Jin Song, Richard Bonneau, Pieter C. Dorrestein, Rob Knight

Issue&Volume: 2019-11-04

Abstract: Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe–metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

DOI: 10.1038/s41592-019-0616-3

Source: https://www.nature.com/articles/s41592-019-0616-3