A phenotype-molecule transfer learning model: Clinical analysis and mechanistic insights of Yinqiao Qingre Tablet in wind-heat syndrome common cold intervention.
5 affiliations
- 1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China.
- 2State Key Laboratory of Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Nanjing 211112, China.
- 3Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
- 4Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- 5China Academy of Chinese Medical Sciences, Beijing 100700, China.
Abstract
Objective
With the advent of the precision medicine era, personalized treatment is becoming an increasingly important trend. However, despite the vast amounts of electronic medical records (EMR) data available, predicting an individual patient's genetic profile remains a significant challenge. Traditional Chinese medicine (TCM) may provide valuable insights into personalized precision medicine. This study aimed to develop a global prediction of syndrome/phenotype-molecule association within personalization (GPS-MAP) model for personalized syndrome/phenotype-molecule association prediction and to evaluate its potential application in identifying new clinical indications and underlying mechanisms of TCM formulas.Methods
GPS-MAP integrates transfer learning, a multi-head mutual-attention mechanism, and large language models (LLMs) for personalized text descriptions. The model was pre-trained on diverse disease/phenotype data and subsequently fine-tuned using a limited TCM syndrome dataset. GPS-MAP was then combined with network target analysis to compare the potential therapeutic advantages of two clinically used TCM formulas and identify potential new clinical subtypes. The computational predictions were further evaluated by clinical Meta-analyses. Finally, in vivo experiments combined with multi-omics analyses were performed to investigate the mechanisms associated with key phenotypes of the newly identified indication.Results
GPS-MAP achieved up to a 39.2% improvement in accuracy compared with two widely used TCM syndrome-molecule association prediction tools, SymMap and SoFDA. Using GPS-MAP and network target analysis, this study identified potential advantages of a TCM formula for treating wind-heat syndrome common cold (WHCC) compared with another clinically used formula and identified a new clinical subtype potentially responsive to this formula. Clinical Meta-analyses supported these predictions, demonstrating a significant clinical advantage of the formula in treating WHCC (P < 0.001) and significantly higher efficacy of YQQRT in the newly identified subtype (P < 0.05). Multi-omics analyses from in vivo experiments further indicated that the formula exerted multi-target effects primarily through restoration of immune regulation.Conclusion
GPS-MAP provides a framework for personalized syndrome/phenotype-molecule association prediction by integrating transfer learning, attention mechanisms, and LLM-derived textual representations. Its application identified a potentially advantageous treatment and a new clinical subtype, while mechanistic analyses suggested that the associated therapeutic effects involve 14-3-3/forkhead box O1 (FOXO1)-centered signaling and the regulation of immune cell homeostasis, maturation, differentiation, and tracking.
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BibTeX
@article{zhang2026phenotypemolecule,
title = {{A phenotype-molecule transfer learning model: Clinical analysis and mechanistic insights of Yinqiao Qingre Tablet in wind-heat syndrome common cold intervention.}},
author = {Tingyu Zhang and Boyang Wang and Dingfan Zhang and Pei Liu and Zhaoyong Zhou and Qinhua Fan and Lei Wang and Rongrong He and Lihao Xiao and Liang Cao and Zhenzhong Wang and Wenquan Su and Shengxian Wu and Weibo Zhao and Qingyuan Liu and Wei Xiao and Shao Li and Yongyan Wang},
journal = {Chinese herbal medicines},
year = {2026},
month = sep,
doi = {10.1016/j.chmed.2026.08.021},
url = {https://europepmc.org/article/MED/42948398},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A phenotype-molecule transfer learning model: Clinical analysis and mechanistic insights of Yinqiao Qingre Tablet in wind-heat syndrome common cold intervention.
AU - Tingyu Zhang
AU - Boyang Wang
AU - Dingfan Zhang
AU - Pei Liu
AU - Zhaoyong Zhou
AU - Qinhua Fan
AU - Lei Wang
AU - Rongrong He
AU - Lihao Xiao
AU - Liang Cao
AU - Zhenzhong Wang
AU - Wenquan Su
AU - Shengxian Wu
AU - Weibo Zhao
AU - Qingyuan Liu
AU - Wei Xiao
AU - Shao Li
AU - Yongyan Wang
PY - 2026
DA - 2026/09/01
JO - Chinese herbal medicines
DO - 10.1016/j.chmed.2026.08.021
UR - https://europepmc.org/article/MED/42948398
AB - Objective With the advent of the precision medicine era, personalized treatment is becoming an increasingly important trend. However, despite the vast amounts of electronic medical records (EMR) data available, predicting an individual patient's genetic profile remains a significant challenge. Traditional Chinese medicine (TCM) may provide valuable insights into personalized precision medicine. This study aimed to develop a global prediction of syndrome/phenotype-molecule association within personalization (GPS-MAP) model for personalized syndrome/phenotype-molecule association prediction and to evaluate its potential application in identifying new clinical indications and underlying mechanisms of TCM formulas.Methods GPS-MAP integrates transfer learning, a multi-head mutual-attention mechanism, and large language models (LLMs) for personalized text descriptions. The model was pre-trained on diverse disease/phenotype data and subsequently fine-tuned using a limited TCM syndrome dataset. GPS-MAP was then combined with network target analysis to compare the potential therapeutic advantages of two clinically used TCM formulas and identify potential new clinical subtypes. The computational predictions were further evaluated by clinical Meta-analyses. Finally, in vivo experiments combined with multi-omics analyses were performed to investigate the mechanisms associated with key phenotypes of the newly identified indication.Results GPS-MAP achieved up to a 39.2% improvement in accuracy compared with two widely used TCM syndrome-molecule association prediction tools, SymMap and SoFDA. Using GPS-MAP and network target analysis, this study identified potential advantages of a TCM formula for treating wind-heat syndrome common cold (WHCC) compared with another clinically used formula and identified a new clinical subtype potentially responsive to this formula. Clinical Meta-analyses supported these predictions, demonstrating a significant clinical advantage of the formula in treating WHCC (P < 0.001) and significantly higher efficacy of YQQRT in the newly identified subtype (P < 0.05). Multi-omics analyses from in vivo experiments further indicated that the formula exerted multi-target effects primarily through restoration of immune regulation.Conclusion GPS-MAP provides a framework for personalized syndrome/phenotype-molecule association prediction by integrating transfer learning, attention mechanisms, and LLM-derived textual representations. Its application identified a potentially advantageous treatment and a new clinical subtype, while mechanistic analyses suggested that the associated therapeutic effects involve 14-3-3/forkhead box O1 (FOXO1)-centered signaling and the regulation of immune cell homeostasis, maturation, differentiation, and tracking.
SN - 1674-6384
SN - 2589-3610
ER -