# Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms

arXiv: https://arxiv.org/abs/2309.15225 · HTML: https://mlsanity.com/paper/2309.15225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 74 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Variational inference for sparse network reconstruction from count data](https://mlsanity.com/paper/1806.03120/) (2018)
- [On Optimal Interpolation In Linear Regression](https://mlsanity.com/paper/2110.11258/) (2021)

## Built on by (most cited first, 1 of 1)

- [Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples](https://mlsanity.com/paper/2509.09413/) (2025)

## Abstract (arXiv)

Microorganisms are found in almost every environment, including the soil, water, air, and inside other organisms, like animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. A lot of research has gone into studying microbial communities in various environments and how their interactions and relationships can provide insights into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our empirical study shows that the proposed method is useful for hyper-parameter selection (training) and comparing the quality of the inferred networks between different algorithms (testing).