Special Issue "Molecular and Cellular Diagnostic Models"

Submission Deadline: 31 January 2021 (closed)
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


  • Phylogenetic analysis of microRNA biomarkers for amyotrophic lateral sclerosis
  • Abstract Amyotrophic lateral sclerosis (ALS), also called Lou Gehrig’s disease, is an irreversible disease that is caused by the degeneration and death of motor neurons. Approximately 5–10% of cases are familial ALS (fALS), and the other cases are sporadic ALS (sALS). Gene mutations have been identified both in fALS and sALS patients. In this study, we discuss the four ALS-related genes, C9orf72, SOD1, FUS, and TARDBP, and review the microRNAs (miRNAs) that are associated with ALS and other neurological disorders from the literature. A phylogenetic analysis is used to explore potential miRNAs that can be taken into account when studying the… More
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  • PathVisio Analysis: An Application Targeting the miRNA Network Associated with the p53 Signaling Pathway in Osteosarcoma
  • Abstract MicroRNAs (miRNAs) are small single-stranded, non-coding RNA molecules involved in the pathogenesis and progression of cancer, including osteosarcoma. We aimed to clarify the pathways involving miRNAs using new bioinformatics tools. We applied WikiPathways and PathVisio, two open-source platforms, to analyze miRNAs in osteosarcoma using miRTar and ONCO.IO as integration tools. We found 1298 records of osteosarcoma papers associated with the word “miRNA”. In osteosarcoma patients with good response to chemotherapy, miR-92a, miR- 99b, miR-193a-5p, and miR-422a expression is increased, while miR-132 is decreased. All identified miRNAs seem to be centered on the TP53 network. This is the first application of… More
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