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Yana Bromberg
Researcher at Rutgers University
Publications - 105
Citations - 4502
Yana Bromberg is an academic researcher from Rutgers University. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 26, co-authored 94 publications receiving 3653 citations. Previous affiliations of Yana Bromberg include University of Alabama at Birmingham & Technische Universität München.
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SNAP: predict effect of non-synonymous polymorphisms on function
Yana Bromberg,Burkhard Rost +1 more
TL;DR: SNAP (screening for non-acceptable polymorphisms), a neural network-based method for the prediction of the functional effects of non-synonymous SNPs, introduced, introducing a well-calibrated measure for the reliability of each prediction.
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PredictProtein—an open resource for online prediction of protein structural and functional features
Guy Yachdav,Edda Kloppmann,László Kaján,Maximilian Hecht,Tatyana Goldberg,Tobias Hamp,Peter Hönigschmid,Andrea Schafferhans,Manfred Roos,Michael Bernhofer,Lothar Richter,Haim Ashkenazy,Marco Punta,Avner Schlessinger,Yana Bromberg,Reinhard Schneider,Gerrit Vriend,Chris Sander,Nir Ben-Tal,Burkhard Rost +19 more
TL;DR: The goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics, and the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures.
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Better prediction of functional effects for sequence variants
TL;DR: SNP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants, significantly outperformed other methods and optimized the new method to perform surprisingly well even without alignments.
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SNAP predicts effect of mutations on protein function
TL;DR: A publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions and is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions.
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Collective judgment predicts disease-associated single nucleotide variants
TL;DR: Here it is found that the Meta-SNP algorithm achieves better performance than the best single predictor, suggesting that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships.