Graph neural network and multi-data heterogeneous networks for microbe-disease prediction

Gong, Houwu; You, Xiong; Jin, Min; Meng, Yajie; Zhang, Hanxue; Yang, Shuaishuai; Xu, Junlin

doi:10.3389/fmicb.2022.1077111

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…The recent advancements in artificial intelligence (AI) have opened new avenues for analyzing microbiome data 123,124 . Among the cutting‐edge techniques, deep neural networks and graph convolutional networks are used to identify complex patterns and interactions within microbial communities 125,126 . Bayesian networks can also be employed to explore the causal links between microbiome and health outcomes 127 .…”

Section: Technological Advances In Breast/gut Microbiome Research: a ...mentioning

confidence: 99%

“… 123 , 124 Among the cutting‐edge techniques, deep neural networks and graph convolutional networks are used to identify complex patterns and interactions within microbial communities. 125 , 126 Bayesian networks can also be employed to explore the causal links between microbiome and health outcomes. 127 The potential of these AI techniques is immense in unveiling critical insights into the role of the microbiome in human health and developing personalized treatments.…”

Section: Technological Advances In Breast/gut Microbiome Research: a ...mentioning

confidence: 99%

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Exploring the human microbiome – A step forward for precision medicine in breast cancer

Jotshi,

Sukla,

Haque

et al. 2023

Cancer Reports

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BackgroundThe second most frequent cancer in the world and the most common malignancy in women is breast cancer. Breast cancer is a significant health concern in India with a high mortality‐to‐incidence ratio and presentation at a younger age.Recent FindingsRecent studies have identified gut microbiota as a significant factor that can have an influence on the development, treatment, and prognosis of breast cancer. This review article aims to describe the influence of microbial dysbiosis on breast cancer occurrence and the possible interactions between oncobiome and specific breast cancer molecular subtypes. The review further also discusses the role of epigenetics and diet/nutrition in the regulation of the gut and breast microbiome and its association with breast cancer prevention, therapy, and recurrence. Additionally, the recent technological advances in microbiome research, including next‐generation sequencing (NGS) technologies, genome sequencing, single‐cell sequencing, and microbial metabolomics along with recent advances in artificial intelligence (AI) have also been reviewed. This is an attempt to present a comprehensive status of the microbiome as a key cancer biomarker.ConclusionWe believe that correlating microbiome and carcinogenesis is important as it can provide insights into the mechanisms by which microbial dysbiosis can influence cancer development and progression, leading to the potential use of the microbiome as a tool for prognostication and personalized therapy.

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Section: Technological Advances In Breast/gut Microbiome Research: a ...mentioning

confidence: 99%

Section: Technological Advances In Breast/gut Microbiome Research: a ...mentioning

confidence: 99%

Exploring the human microbiome – A step forward for precision medicine in breast cancer

Jotshi,

Sukla,

Haque

et al. 2023

Cancer Reports

View full text Add to dashboard Cite

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Risk factors and drug discovery for cognitive impairment in type 2 diabetes mellitus using artificial intelligence interpretation and graph neural networks

Zhang,

Xie,

You

et al. 2023

Front. Endocrinol.

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BackgroundAmong the 382 million diabetic patients worldwide, approximately 30% experience neuropathy, and one-fifth of these patients eventually develop diabetes cognitive impairment (CI). However, the mechanism underlying diabetes CI remains unknown, and early diagnostic methods or effective treatments are currently not available.ObjectiveThis study aimed to explore the risk factors for CI in patients with type 2 diabetes mellitus (T2DM), screen potential therapeutic drugs for T2DM-CI, and provide evidence for preventing and treating T2DM-CI.MethodsThis study focused on the T2DM population admitted to the First Affiliated Hospital of Hunan College of Traditional Chinese Medicine and the First Affiliated Hospital of Hunan University of Chinese Medicine. Sociodemographic data and clinical objective indicators of T2DM patients admitted from January 2018 to December 2022 were collected. Based on the Montreal Cognitive Assessment (MoCA) Scale scores, 719 patients were categorized into two groups, the T2DM-CI group with CI and the T2DM-N group with normal cognition. The survey content included demographic characteristics, laboratory serological indicators, complications, and medication information. Six machine learning algorithms were used to analyze the risk factors of T2DM-CI, and the Shapley method was used to enhance model interpretability. Furthermore, we developed a graph neural network (GNN) model to identify potential drugs associated with T2DM-CI.ResultsOur results showed that the T2DM-CI risk prediction model based on Catboost exhibited superior performance with an area under the receiver operating characteristic curve (AUC) of 0.95 (specificity of 93.17% and sensitivity of 78.58%). Diabetes duration, age, education level, aspartate aminotransferase (AST), drinking, and intestinal flora were identified as risk factors for T2DM-CI. The top 10 potential drugs related to T2DM-CI, including Metformin, Liraglutide, and Lixisenatide, were selected by the GNN model. Some herbs, such as licorice and cuscutae semen, were also included. Finally, we discovered the mechanism of herbal medicine interventions in gut microbiota.ConclusionThe method based on Interpreting AI and GNN can identify the risk factors and potential drugs associated with T2DM-CI.

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A study on pharmaceutical text relationship extraction based on heterogeneous graph neural networks

Zou,

Liu,

Wang

et al. 2023

MBE

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<abstract> <p>Effective information extraction of pharmaceutical texts is of great significance for clinical research. The ancient Chinese medicine text has streamlined sentences and complex semantic relationships, and the textual relationships may exist between heterogeneous entities. The current mainstream relationship extraction model does not take into account the associations between entities and relationships when extracting, resulting in insufficient semantic information to form an effective structured representation. In this paper, we propose a heterogeneous graph neural network relationship extraction model adapted to traditional Chinese medicine (TCM) text. First, the given sentence and predefined relationships are embedded by bidirectional encoder representation from transformers (BERT fine-tuned) word embedding as model input. Second, a heterogeneous graph network is constructed to associate words, phrases, and relationship nodes to obtain the hidden layer representation. Then, in the decoding stage, two-stage subject-object entity identification method is adopted, and the identifier adopts a binary classifier to locate the start and end positions of the TCM entities, identifying all the subject-object entities in the sentence, and finally forming the TCM entity relationship group. Through the experiments on the TCM relationship extraction dataset, the results show that the precision value of the heterogeneous graph neural network embedded with BERT is 86.99% and the F1 value reaches 87.40%, which is improved by 8.83% and 10.21% compared with the relationship extraction models CNN, Bert-CNN, and Graph LSTM.</p> </abstract>

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Graph neural network and multi-data heterogeneous networks for microbe-disease prediction

Cited by 3 publications

References 27 publications

Exploring the human microbiome – A step forward for precision medicine in breast cancer

Exploring the human microbiome – A step forward for precision medicine in breast cancer

Risk factors and drug discovery for cognitive impairment in type 2 diabetes mellitus using artificial intelligence interpretation and graph neural networks

A study on pharmaceutical text relationship extraction based on heterogeneous graph neural networks

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