To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Pattern-recognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on the pattern recognition of multivariate data, which are generated using an array of multiple probes that cross-reactively interact with analytes. This sensing approach is significantly different from conventional specific cell sensing based on highly specific probes, including antibodies against biomarkers. Encouraged by the advantages of this technique, such as the simplicity, rapidity, and tunability of the systems without requiring a priori knowledge of biomarkers, numerous sensor arrays have been developed over the past decade and used in a variety of cell sensing applications; these include disease diagnosis, drug discovery, and fundamental research. This review summarizes recent progress in pattern-recognition-based cell sensing, with a particular focus on guidelines for designing materials and arrays, techniques for analyzing response patterns, and applications of sensor systems that are focused primarily for the biomedical field.

为了捕获更广泛的复杂生物现象，需要基于细胞检测和表征特异性的传统传感替代方案。基于模式识别的传感是一种分析方法，旨在模仿哺乳动物的感觉系统，基于多变量数据的模式识别来识别分析物，这些数据是使用与分析物交叉反应的多个探针阵列生成的。这种传感方法与基于高度特异性探针（包括针对生物标志物的抗体）的传统特异性细胞传感显着不同。受该技术优点的鼓舞，例如系统的简单性、快速性和可调谐性，无需生物标志物的先验知识，在过去的十年中已经开发了许多传感器阵列，并用于各种细胞传感应用；这些包括疾病诊断、药物发现和基础研究。这篇综述总结了基于模式识别的细胞传感的最新进展，特别关注设计材料和阵列的指南、分析响应模式的技术以及主要针对生物医学领域的传感器系统的应用。