Title: Friend or Foe Show Chinese title

Title: 朋友还是敌人

Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions. With recent advances in genome-scale metabolic models, we are now capable of simulating interactions between thousands of pairs of bacteria in thousands of different environmental settings at a scale infeasible experimentally. These approaches can generate tremendous amounts of data that can be exploited by state-of-the-art machine learning algorithms to uncover the mechanisms driving interactions. Here, we present Friend or Foe, a compendium of 64 tabular environmental datasets, consisting of more than 26M shared environments for more than 10K pairs of bacteria sampled from two of the largest collections of metabolic models. The Friend or Foe datasets are curated for a wide range of machine learning tasks -- supervised, unsupervised, and generative -- to address specific questions underlying bacterial interactions. We benchmarked a selection of the most recent models for each of these tasks and our results indicate that machine learning can be successful in this application to microbial ecology. Going beyond, analyses of the Friend or Foe compendium can shed light on the predictability of bacterial interactions and highlight novel research directions into how bacteria infer and navigate their relationships. Show Chinese abstract

Abstract: 微生物生态学中的一个基本挑战是确定细菌在不同环境条件下是竞争还是合作。 随着基因组规模代谢模型的最新进展，我们现在能够以实验上不可行的规模模拟数千对细菌在数千种不同环境设置中的相互作用。 这些方法可以生成大量数据，这些数据可以被最先进的机器学习算法利用，以揭示驱动相互作用的机制。 在此，我们介绍了Friend or Foe，这是一个包含64个表格环境数据集的综合数据集，其中包括超过2600万共享环境，覆盖来自两个最大代谢模型集合的超过10000对细菌。 Friend or Foe数据集针对一系列机器学习任务——监督学习、无监督学习和生成学习——来解决细菌相互作用背后的特定问题。 我们对每项任务中最新的模型进行了基准测试，我们的结果表明机器学习在微生物生态学的应用中可以取得成功。 进一步分析Friend or Foe综合数据集可以揭示细菌相互作用的可预测性，并突出细菌如何推断和导航其关系的新研究方向。