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Soft sensors are widely used in industrial systems to monitor key variables that are difficult to measure, using measurements from other available physical sensors. This thesis aims to develop efficient modeling strategies using deep learning methods to construct data-driven soft sensors that are both accurate and reliable. The outcomes of this research have significant impacts towards advancing the state-of-the-art of deep learning-based soft sensors by enhancing their practicality in modern industries.

Additional file 3: Fig. S2. A univariate cox analysis of every 75 TRIM proteins based on TCGA-LIHC dataset with p < 0.05.

Additional file 1: Fig. S1. Oncoprint plot of all somatic mutations of TRIM proteins in TCGA-LIHC using cbioportal database.