دورية أكاديمية
A graph neural network approach for predicting drug susceptibility in the human microbiome.
| العنوان: | A graph neural network approach for predicting drug susceptibility in the human microbiome. |
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| المؤلفون: | Maryam; Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea., Rehman MU; Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, United Arab Emirates., Hussain I; Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, United Arab Emirates., Tayara H; School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea. Electronic address: hilaltayara@jbnu.ac.kr., Chong KT; Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea. Electronic address: kitchong@jbnu.ac.kr. |
| المصدر: | Computers in biology and medicine [Comput Biol Med] 2024 Sep; Vol. 179, pp. 108729. Date of Electronic Publication: 2024 Jul 01. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| مواضيع طبية MeSH: | Gastrointestinal Microbiome*/drug effects , Neural Networks, Computer*, Humans ; Microbiota/drug effects ; Machine Learning ; Deep Learning |
| مستخلص: | Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Bioinformatics; Graph neural network; Microbiome; Molecular docking |
| تواريخ الأحداث: | Date Created: 20240702 Date Completed: 20240814 Latest Revision: 20240814 |
| رمز التحديث: | 20240814 |
| DOI: | 10.1016/j.compbiomed.2024.108729 |
| PMID: | 38955124 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1879-0534 |
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| DOI: | 10.1016/j.compbiomed.2024.108729 |