Guest Editors
Dr. Binwu Ying, Sichuan University, yingbinwu@scu.edu.cn
Dr. Wei Xu, University of Toronto, Wei.Xu@uhnres.utoronto.ca
Dr. Mengyuan Lyu (Assistant Guest Editor), Sichuan University, mengyuanlvscu@foxmail.com
Summary
The development of molecular biology and cytology promotes the progress of traditional medicine to personalized medicine. At the same time, the evolution of computer technology accelerates machine learning and bioinformatics to be increasingly integrated into molecular biology and cytology and thus drives a milestone shift in these two fields, especially in traditionally molecular and cellular diagnostic pattern.
Nowadays, more and more scholars prefer to construct molecular or cellular diagnostic models due to high specificity, rapid testing and the contribution of revealing the pathogenesis of targeted diseases. Some excellent molecular and cellular diagnostic models have been reported and exhibit outstanding performance in original training and testing sets. However, practical problems are obliged to be considered, such as the generalization ability of models, practicality in resource-limited regions and the feasibility of simultaneously measuring variables included in models. In this Special Issue, BIOCELL will focus on researches that build molecular and cellular diagnostic model for diseases, compare performance of different molecular and cellular diagnostic models for a given disease or evaluate reported molecular and cellular diagnostic models from various perspectives. Any innovative researches are encouraged!
Keywords
Molecular Biology and Cytology, Diagnostic Model, Machine Learning
Published Papers
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