1/9/2024 0 Comments Automute protein![]() Third, try to review the conversations under the Muted label 1-2 times a day at the most.The unread label for the Muted label is intentionally subtle so you wouldn’t feel any guilt for the number there. This is a trick we learned from our CEO – in this way, you will not forget to take a look at the muted conversations every now and then. Secondly, you may want to drag the Muted label above Recent on the left pane and collapse it.First of all you’ll have to configure it in the Account preferences, as it is turned off by default.How to make the most out of Automute in Fleep? Note that the “stranger(s)” in the new conversation will be added to your contacts only if you reply into that new conversation or when someone among your contacts adds you to a conversation with the “stranger(s)”. More specifically, if the incoming email message was sent by someone who is not in your contacts, the newly created conversation is automatically muted. people who are not in your contact list) do not get your attention unless you want to give them your attention. The goal of Automute is to make sure conversations created by “strangers” (i.e. You will not receive any notifications about them, but the conversations will remain unread until you can read them at your own convenience. If Automute is enabled, all of the automatically muted incoming email conversations will be visible under the Muted label. This is why we have built the Automute feature - the option to have Fleep automatically mute all incoming emails that are sent from a new email address (that is, an email address that is not among your Fleep contacts). Sometimes, they’re worth your attention – other times, not so much. Whoever has been able to find out your email address can try to get your attention. One of the reasons why email tends to be a drain on your productivity is that anyone can attempt to reach you via email. It is very fast making it capable of analyzing large variant datasets. PON-P2 is a powerful tool for screening harmful variants and for ranking and prioritizing experimental characterization. ![]() The coverage of PON-P2 is 61.7% in the 10-fold cross-validation and 62.1% in the test dataset. In 10-fold cross-validation test, its accuracy and MCC are 0.90 and 0.80, respectively, and in the independent test, they are 0.86 and 0.71, respectively. PON-P2 consistently showed superior performance in comparison to existing state-of-the-art tools. Extensive feature selection was performed to identify 8 informative features among altogether 622 features. PON-P2 utilizes information about evolutionary conservation of sequences, physical and biochemical properties of amino acids, GO annotations and if available, functional annotations of variation sites. PON-P2 is trained using pathogenic and neutral variants obtained from VariBench, a database for benchmark variation datasets. The method is a machine learning-based classifier and groups the variants into pathogenic, neutral and unknown classes, on the basis of random forest probability score. We have developed a new computational tool, PON-P2, for classification of amino acid substitutions in human proteins. More reliable and faster prediction methods are needed to interpret enormous amounts of data generated by sequencing and genome projects.
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