Detection of Cellular Spikes and Classification of Cells from Raw Nanoscale Biosensor Data

摘要: Nanoscale devices have provided promising endeavors for detecting crucial biomarkers such as DNA, proteins, and human cells at a finer scale. These can improve prognosis by dreadful disease cancer an early stage than the current approaches. Analyzing raw data from these nanoscale detection is tedious suffers noise. Furthermore, decisions are made based on manual or semi-automated analysis—which time-consuming, monotonous error-prone process. Recent trends show unprecedented growth in advancement of nanotechnology medical diagnosis. generate huge amount analyzing order to classify fully automated robust way challenge. In this paper, we present algorithm identifying cellular spikes, adapted extreme learning machines dynamic time warping classification collected biosensors, solid-state micropores. Our approach with accuracy 95.6%, precision recall 85.7% 80.0%, respectively.