明智的人 发表于 2025-3-25 05:18:33
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 wiAncillary 发表于 2025-3-25 11:31:13
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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 theMercantile 发表于 2025-3-25 16:30:38
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.背心 发表于 2025-3-25 22:05:06
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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