分类器(UML)
计算生物学
基因表达谱
基因表达
工作流程
模块化设计
分子诊断学
计算机科学
个性化医疗
基因
生物信息学
生物
人工智能
遗传学
操作系统
数据库
作者
Li Zhang,Qian Liu,Yongcan Guo,Liyan Tian,Kena Chen,Dan Bai,Hongyan Yu,Xiaole Han,Wenxin Luo,Tao Feng,Shixiong Deng,Guoming Xie
标识
DOI:10.1186/s12951-024-02445-0
摘要
Abstract Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.
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