Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Srinivasan, Shanchana; Jnana, Apoorva; Murali, Thokur Sreepathy

doi:10.1007/s00248-024-02370-7

Cited by 3 publications

(1 citation statement)

References 106 publications

(138 reference statements)

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“…Direct pathogen detection confirms the association with diseases by detecting pathogenic microbes in patient samples 50 ; microbiome studies utilize high‐throughput sequencing technologies to compare microbial composition differences between patients and healthy control groups 51 ; while host–microbe interaction network analysis constructs interaction networks by integrating host and microbiome data. 52 However, these methods also have some limitations. For instance, direct pathogen detection may overlook some potential microbial pathogenic factors, microbiome studies are limited by sample quantity and quality, and the complexity of host–microbe interaction network analysis may result in difficulties in interpreting the results.…”

Section: Introductionmentioning

confidence: 99%

Predicting potential microbe–disease associations based on dual branch graph convolutional network

Chen,

Zhu,

Yuan

2024

J Cellular Molecular Medi

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Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time‐consuming and costly nature of laboratory‐based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe–disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe–disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine‐tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five‐fold cross‐validation (5‐fold‐CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5‐fold‐CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe–disease associations.

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Section: Introductionmentioning

confidence: 99%