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Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression.
Xie, Jize; Zheng, Xubin; Yan, Jianlong; Li, Qizhi; Jin, Nana; Wang, Shuojia; Zhao, Pengfei; Li, Shuai; Ding, Wanfu; Cheng, Lixin; Geng, Qingshan.
Afiliación
  • Xie J; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Zheng X; John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China.
  • Yan J; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Li Q; Great Bay University, Dongguan, China.
  • Jin N; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Wang S; John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China.
  • Zhao P; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Li S; Health Data Science Center, Shenzhen People's Hospital, Shenzhen 518020, China.
  • Ding W; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Cheng L; Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
  • Geng Q; John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China.
iScience ; 27(6): 109908, 2024 Jun 21.
Article en En | MEDLINE | ID: mdl-38827397
ABSTRACT
Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos