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Principal Investigator
Name
Yuan Dan
Degrees
M.P.H.
Institution
Wuhan University of Science and Technology
Position Title
Graduate student
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-991
Initial CDAS Request Approval
Dec 6, 2022
Title
Development of anti-lung cancer screening system based on target recognition and natural language
Summary
The characteristics and advantages of combination of traditional Chinese and western medicine include enhanced curative effect and reduced toxic and side effects. However, due to the complex interaction between traditional Chinese and western medicine, incompatibility of combination of traditional Chinese and western medicine has emerged. At present, the drug combination research based on artificial intelligence technology mainly uses single-scale data such as literature data, drug target, etc., ignores internal correlation of multi-scale data, and thus cannot establish a multi-scale drug effect projecting model, which restricts performance improvement of the screening system of drug combination.
Aims

This project will first use a previously established target identification platform to identify the targets of anti-lung cancer traditional Chinese medicine. Then, a Boolean network model and a previously built TAIGET computing platform will be used to identify the key genes of western medicine. Based on PPI networks, cell signaling modules consisting of the targets of traditional Chinese medicine and the key genes of western medicine will be constructed, which is further integrated with side-effect markers of lung cancer treatment, with the aim to complete multi-scale drug effect projecting. Thus, a screening system for combination of Chinese and Western medicines against lung cancer will be established for the successful screening of combination of Chinese and Western medicines with potential drug effects and small side-effects. Cell and mouse experiments will be used to verify the compatibility of combination of Chinese and Western medicines. The project aims to help medical workers or patients to quickly screen drugs, avoid or reduce adverse events, and promote clinical rational drug use.

Collaborators

Xv Xue, xuexu007@wust.edu.cn, Wuhan University of Science and Technology
Li Fangxv, li_fangxu@163.com, Wuhan University of Science and Technology
Huang Hangxing, huanghangxing97@163.com, Wuhan University of Science and Technology