Publication Name	World Journal of Transplantation
Manuscript ID	114000
Country	China

Category	Transplantation
Manuscript Type	Minireviews
Article Title	Application of machine learning in the research progress of post-kidney transplant rejection
Manuscript Source	Invited Manuscript
All Author List	Yun-Peng Guo, Quan Wen, Yu-Yang Wang, Gai Hang and Bo Chen
Funding Agency and Grant Number
Corresponding Author	Bo Chen, Chief Physician, MD, PhD, Professor, Department of Urinary Surgery, Tongliao People's Hospital, No. 668 Horqin Street, Horqin District, Tongliao 028000, Inner Mongolia Autonomous Region, China. chenmuxin@126.com
Key Words	Machine learning; Kidney transplant; Rejection; Predictive models; Biomarkers; Pathological image analysis; Immune cell infiltration; Precision medicine
Core Tip	Recent advances in machine learning (ML) have opened new avenues for the early prediction and precise diagnosis of rejection in kidney transplantation. ML techniques can analyze large, complex datasets to identify patterns and correlations that may not be readily apparent through conventional analytical methods. By leveraging diverse data sources, including clinical, laboratory, and imaging data, ML models can provide more accurate risk assessments and facilitate timely interventions to mitigate the risk of rejection.
Citation	Guo YP, Wen Q, Wang YY, Hang G, Chen B. Application of machine learning in the research progress of post-kidney transplant rejection. World J Transplant 2025; In press

Received	2025-09-09 02:22
Peer-Review Started	2025-09-09 02:22
First Decision by Editorial Office Director	2025-09-25 08:17
Return for Revision	2025-09-25 08:17
Revised	2025-10-08 09:56
Publication Fee Transferred
Second Decision by Editor	2025-12-23 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2025-12-23 06:27
Articles in Press	2025-12-23 06:27
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-3230 (online)
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