Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Abstract

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Deep-Learning-Guided Electrochemical Impedance Spectroscopy for Calibration-Free Pharmaceutical Moisture Content Monitoring

“…Additionally, electrochemical simulation sheds light on the underlying mechanism of moisture monitoring via EIS signals. To enhance the accuracy and effectiveness of moisture content monitoring, we propose the utilization of big data , and deep learning techniques, ,,− 1D convolutional neural network (1DCNN) for signal processing, and the development of a predictive model . Due to the prevalence of AI technologies, deep learning has proven to make outstanding contributions across different disciplines. − The choice of 1DCNN is driven by its excellence in handling time-series data and extracting key features from complex signals, ideal for EIS data interpretation.…”