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Reliability analysis using error probabilities for combinational logic circuits has been investigated widely in the literature. Reliability analysis for sequential logic circuits using these methods would be inaccurate because of existence of loops in their architecture. In this paper a new method based on conversion of sequential circuit to combinational one and applying an iterative reliability analysis is developed. A Monte Carlo method-based reliability analysis is introduced for sequential circuits, which is used for first method validation. Experimental results demonstrate good accuracy of the method.

O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal–Wallis (K–W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.