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

• URL: http://arxiv.org/abs/2309.15225v1
• Date: Tue, 26 Sep 2023 19:43:15 GMT
• Title: Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms
• Authors: Daniel Agyapong, Jeffrey Ryan Propster, Jane Marks, Toby Dylan Hocking
• Abstract summary: Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples. 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.
• Score: 1.8638865257327277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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