Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice

Author:

Chen Wanling12,Zhang Peng3,Zhang Xueli4,Xiao Tiantian5,Zeng Jianhai1,Guo Kaiping6,Qiu Huixian1,Cheng Guoqiang3,Wang Zhangxing4,Zhou Wenhao7,Zeng Shujuan1,Wang Mingbang89ORCID

Affiliation:

1. Division of Neonatology, Longgang Central Hospital of Shenzhen, Shenzhen, China

2. Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China

3. Division of Neonatology, Children’s Hospital of Fudan University, Shanghai, China

4. Division of Neonatology, Shenzhen Longhua People’s Hospital, Shenzhen, China

5. Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China

6. Division of Pediatric, Longgang Central Hospital of Shenzhen, Shenzhen, China

7. Division of Neonatology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China

8. Department of Neonatology, Longgang Maternity and Child Institute of Shantou University Medical College (Longgang District Maternity & Child Healthcare Hospital of Shenzhen City), Shenzhen, China

9. Microbiome Therapy Center, Department of Experiment & Research, South China Hospital, Medical School, Shenzhen University, Shenzhen, China

Funder

Natural Science Foundation of Guangdong Province

Shenzhen Science Technology and Innovation Commission

National Natural Science Foundation of China

Longgang Science Technology and Innovation Commission of Shenzhen

Longgang District Maternity & Child Healthcare Hospital of Shenzhen City

Longgang Central Hospital of Shenzhen

Sichuan Natural Science Foundation

Publisher

Informa UK Limited

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