Improving the Efficiency of Early Cancer Detection using Single-Cell ATAC-seq Data for Internet of Biomedical Things

As is well-known, early cancer detection plays a crucial role in successful treatment. Since the early 2000s, wireless biomedical sensors have shown promise in this area by detecting cancer cells through the identification of protein-level alterations. Moreover, with the decreasing costs of genetic analysis, it is becoming increasingly feasible to envision the development of biomedical IoT devices shortly. These devices would be capable of real-time analysis of biological samples (blood, urine, etc.) and, by taking into account an individual’s medical history, could assess the risk of cancer presence. In this research, we demonstrate that by improving the classification speed of single-cell ATAC-seq data using machine learning methods, we can pave the way for close to real-time cancer risk assessment via the classification of samples collected by IoT devices. Among evaluating six well-known methods for classifying these data, we show that our proposed method, can achieve similar classification accuracy to the state-of-the-art single-cell ATAC-seq data analysis methods, while requiring only about a quarter of the processing time. The proposed method can provide an efficient method for rapid cancer monitoring on Internet of Biomedical Things.