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Yaron Orenstein
Researcher at Ben-Gurion University of the Negev
Publications - 65
Citations - 995
Yaron Orenstein is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 16, co-authored 54 publications receiving 746 citations. Previous affiliations of Yaron Orenstein include Tel Aviv University & Massachusetts Institute of Technology.
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Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models
TL;DR: This work resequenced data from previously published HT‐SELEX experiments, the most extensive mammalian TF–DNA binding data available to date, to reveal the nucleotide position‐dependent DNA shape readout in TF‐binding sites and the TF family‐specific position dependence.
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A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data
Yaron Orenstein,Ron Shamir +1 more
TL;DR: A large-scale comparison between implementations of these two in vitro technologies is possible and the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX are assessed and how well these models predict in vivo binding is measured.
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ElemeNT: a computational tool for detecting core promoter elements.
Anna Sloutskin,Yehuda M. Danino,Yaron Orenstein,Yonathan Zehavi,Tirza Doniger,Ron Shamir,Tamar Juven-Gershon +6 more
TL;DR: The Elements Navigation Tool (ElemeNT) is a user-friendly web-based, interactive tool for prediction and display of putative core promoter elements and their biologically-relevant combinations, and the CORE database summarizes ElemeNT-predicted core promoter Elements near CAGE and RNA-seq-defined Drosophila melanogaster transcription start sites (TSSs).
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Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding
Daniel D. Le,Tyler C. Shimko,Arjun K. Aditham,Allison M. Keys,Scott A. Longwell,Yaron Orenstein,Polly M. Fordyce +6 more
TL;DR: A high-throughput assay and analysis pipeline capable of measuring binding energies for over one million sequences with high resolution and establishing that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior is presented.
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A deep neural network approach for learning intrinsic protein-RNA binding preferences.
TL;DR: DLPRB (Deep Learning for Protein‐RNA Binding), a new deep neural network (DNN) approach for learning intrinsic protein‐RNA binding preferences and predicting novel interactions, is developed and results in inferring accurate RNA‐binding models from high‐throughput in vitro data exhibit substantial improvements.