FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule

Wuai Zhou; Kuo Yang; Jianyang Zeng; Xinxing Lai; Xin Wang; Chaofan Ji; Yan Li; Peng Zhang; Shao Li

doi:10.1016/j.phrs.2021.105752

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule

Pharmacol Res. 2021 Nov:173:105752. doi: 10.1016/j.phrs.2021.105752. Epub 2021 Sep 2.

Authors

Wuai Zhou¹, Kuo Yang¹, Jianyang Zeng², Xinxing Lai³, Xin Wang¹, Chaofan Ji⁴, Yan Li⁴, Peng Zhang¹, Shao Li⁵

Affiliations

¹ Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, 100084 Beijing, China.
² Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
³ Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, 100084 Beijing, China; Institute for Brain Disorders, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
⁴ Department of Traditional Chinese Medicine, Yijishan Hospital of Wannan Medical College, Wuhu, 241000 Anhui, China.
⁵ Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, 100084 Beijing, China. Electronic address: shaoli@mail.tsinghua.edu.cn.

PMID: 34481072
DOI: 10.1016/j.phrs.2021.105752

Abstract

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from "experience based, macro" to "data based, macro-micro combined" as well as the development of TCM network pharmacology.

Keywords: Deep neural network; Intelligent recommendation; Network pharmacology; TCM Master; TCM formula.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Medicine, Chinese Traditional*
Network Pharmacology
Neural Networks, Computer*
Phenotype