Cristian A. Yones

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miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input(More)
The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to(More)
Motivation Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction(More)
Objective: The aim of this work was to evaluate the vaginal content of women attending family planning controls and its relationship with the contraceptive practice used (oral contraceptive pills, intrauterine device, condoms, or rhythm method) as well as its relationship with the lack of regular contraception. Design: Observational, descriptive study.(More)
The computational prediction of novel microRNAs (miRNAs) within a full genome involves identifying sequences having the highest chance of being bona fide miRNA precursors (pre-miRNAs). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to(More)
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