Titlebook: Computational Methods for Predicting Post-Translational Modification Sites; Dukka B. KC Book 2022 The Editor(s) (if applicable) and The Au

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Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifiernamed “RF-Prx” based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated wi

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Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL,te Deep Learner for lysine PTMs). Specifically, MUscADEL employs bidirectional long short-term memory (BiLSTM) recurrent neural networks and is capable of predicting eight major types of lysine PTMs in both the human and mouse proteomes. The web server of MUscADEL is publicly available at . for the

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Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMappere present a workflow to visualize histone proteins and their myriad of PTMs based on different R visualization modules applied to data from quantitative middle-down experiments. The procedure can be adapted to diverse experimental designs and is applicable to different proteins and PTMs.

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J. R. McFarlane,M. Mullin,E. Jacksonnamed “RF-Prx” based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated wi