The recent focus on microbes in human medicine highlights their potential
role in the genetic framework of diseases. To decode the complex interactions
among genes, microbes, and diseases, computational predictions of
gene-microbe-disease (GMD) associations are crucial. Existing methods primarily
address gene-disease and microbe-disease associations, but the more intricate
triple-wise GMD associations remain less explored. In this paper, we propose a
Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD
associations. HCMGNN constructs a heterogeneous graph linking genes, microbes,
and diseases through their pairwise associations, and utilizes six predefined
causal metapaths to extract directed causal subgraphs, which facilitate the
multi-view analysis of causal relations among three entity types. Within each
subgraph, we employ a causal semantic sharing message passing network for node
representation learning, coupled with an attentive fusion method to integrate
these representations for predicting GMD associations. Our extensive
experiments show that HCMGNN effectively predicts GMD associations and
addresses association sparsity issue by enhancing the graph's semantics and
structure.

利用异构因果元路径图神经网络 (HCMGNN) 预测基因 - 微生物 - 疾病 (GMD) 关联，并通过提升图的语义和结构来解决关联稀疏性问题。

基于异质因果元路径图神经网络的基因 - 微生物 - 疾病关联预测

Heterogeneous Causal Metapath Graph Neural Network for  Gene-Microbe-Disease Association Prediction

The community of different types of microbes present in a biological niche
plays a very important role in functioning of the system. The crosstalk or
interactions among the different microbes contributes to the building blocks of
such microbial community structures. Evidence reported in biomedical text
serves as a reliable source for predicting such interactions. However, going
through the vast and ever-increasing volume of biomedical literature is an
intimidating and time consuming process. This necessitates development of
automated methods capable of accurately extracting bacterial relations reported
in biomedical literature. In this paper, we introduce a method for automated
extraction of microbial interactions (specifically between bacteria) from
biomedical literature along with ways of using transfer learning to improve its
accuracy. We also describe a pipeline using which relations among specific
bacteria groups can be mined. Additionally, we introduce the first publicly
available dataset which can be used to develop bacterial interaction extraction
methods.

本文研究了从生物医学文献中自动提取微生物交互作用的方法，使用迁移学习等技术提高了其准确性，并且介绍了可以用于开发微生物相互作用提取方法的第一个公开可用的数据集。

BactInt: 基于领域驱动的迁移学习方法和用于从生物医学文本中提取细菌间相互作用的语料库

BactInt: A domain driven transfer learning approach and a corpus for  extracting inter-bacterial interactions from biomedical text

Microbes are everywhere, including in and on our bodies, and have been shown
to play key roles in a variety of prevalent human diseases. Consequently, there
has been intense interest in the design of bacteriotherapies or "bugs as
drugs," which are communities of bacteria administered to patients for specific
therapeutic applications. Central to the design of such therapeutics is an
understanding of the causal microbial interaction network and the population
dynamics of the organisms. In this work we present a Bayesian nonparametric
model and associated efficient inference algorithm that addresses the key
conceptual and practical challenges of learning microbial dynamics from time
series microbe abundance data. These challenges include high-dimensional (300+
strains of bacteria in the gut) but temporally sparse and non-uniformly sampled
data; high measurement noise; and, nonlinear and physically non-negative
dynamics. Our contributions include a new type of dynamical systems model for
microbial dynamics based on what we term interaction modules, or learned
clusters of latent variables with redundant interaction structure (reducing the
expected number of interaction coefficients from $O(n^2)$ to $O((\log n)^2)$);
a fully Bayesian formulation of the stochastic dynamical systems model that
propagates measurement and latent state uncertainty throughout the model; and
introduction of a temporally varying auxiliary variable technique to enable
efficient inference by relaxing the hard non-negativity constraint on states.
We apply our method to simulated and real data, and demonstrate the utility of
our technique for system identification from limited data and gaining new
biological insights into bacteriotherapy design.

通过 Bayesian 非参数模型及其相关的高效推理算法，本文解决了从时间序列细菌计数数据中学习微生物动态学的关键概念和实际挑战，并将其应用于模拟和实际数据，展示了从有限数据中系统识别与获取新的生物学见解的功能。