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Esmeralda Vicedo

Researcher at Technische Universität München

Publications -  14
Citations -  1188

Esmeralda Vicedo is an academic researcher from Technische Universität München. The author has contributed to research in topics: Protein function prediction & Heat shock protein. The author has an hindex of 9, co-authored 14 publications receiving 1049 citations. Previous affiliations of Esmeralda Vicedo include Ludwig Maximilian University of Munich & University of Warsaw.

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A large-scale evaluation of computational protein function prediction

Predrag Radivojac, +107 more
- 01 Mar 2013 - 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
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Protein disorder — a breakthrough invention of evolution?

TL;DR: It is more useful to picture disorder as a distinct phenomenon in structural biology than as an extreme example of protein flexibility, and it seems advantageous to portray the universe of all possible proteins in terms of two main types: well-structured, disordered.
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Homology-based inference sets the bar high for protein function prediction.

TL;DR: This work describes a few methods that predict protein function exclusively through homology and proposes a new rigorous measure to compare predicted and experimental annotations that puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users.
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Hands-on tutorial for parallel computing with R

TL;DR: This tutorial gives a short, practical overview of four, in view of the authors, important packages for parallel computing in R, namely multicore, snow, snowfall and nws.
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Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

TL;DR: The release of PredictProtein for the Debian operating system and derivatives covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility, nuclear localization signals, and intrinsically disordered regions.