# Friend or Foe

## TL;DR

This paper introduces 'Friend or Foe,' a comprehensive dataset of bacterial interactions derived from genome-scale metabolic models, enabling machine learning to uncover interaction mechanisms and predict bacterial relationships across diverse environments.

## Contribution

The paper provides a large, curated dataset of bacterial interaction simulations and benchmarks machine learning models, advancing microbial ecology research.

## Key findings

- Machine learning models can effectively predict bacterial interactions.
- The dataset enables analysis of interaction predictability.
- New research directions in bacterial relationship inference.

## 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.