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In this study, we cast the problem of miRNA prediction as a problem of information retrieval where novel miRNAs were retrieved by the known miRNAs (as query samples) from a genome-scale pool of candidate sequences that can form hairpinned structures.
Supervised features were successfully used in the context of image classification and retrieval, where they showed excellent results.
Authors in [36] present a grid-based framework for image retrieval where the images are partitioned into blocks.
Such concerns are multiplied in the emergency department (ED) and during retrieval where patients may be unstable, preparation or preoxygenation time limited and the environment uncontrolled.
This method can reduce the sensitivity of changing the training sets or real-world large-scale retrieval where images are added to the database with time.
Such an approach has major drawbacks in the context of interactive retrieval, where the user iteratively builds the training data set in a semi-supervised approach by providing positive and negative annotations to the system in the relevance feedback loop.
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This is corroborated by results such as [26], a work on cross-language information-retrieval, where bag-of-words representations of translated documents are successfully used.
They then completed a retrieval task where old (studied) and new words were presented.
There are retrieval approaches where the imperfect definition of similarity is circumvented differently.
As a result, the overall retrieval performance, where speed is a primary concern, can be significantly improved.
Query by example is an audio retrieval task where a user provides an example signal and the retrieval system returns similar samples from the database.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com