D
Doron Stupp
Researcher at Hebrew University of Jerusalem
Publications - 9
Citations - 198
Doron Stupp is an academic researcher from Hebrew University of Jerusalem. The author has contributed to research in topics: Phylogenetic profiling & Computer science. The author has an hindex of 4, co-authored 7 publications receiving 119 citations.
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Journal ArticleDOI
Synthetic RNA-Based Immunomodulatory Gene Circuits for Cancer Immunotherapy
Lior Nissim,Ming-Ru Wu,Erez Pery,Adina Binder-Nissim,Hiroshi I. Suzuki,Doron Stupp,Claudia Wehrspaun,Yuval Tabach,Phillip A. Sharp,Timothy K. Lu +9 more
TL;DR: A proof-of-concept immunomodulatory gene circuit platform that enables tumor-specific expression of immunostimulators, which could potentially overcome limitations of cancer immunotherapy.
Journal ArticleDOI
A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)
Ming-Ru Wu,Lior Nissim,Doron Stupp,Erez Pery,Adina Binder-Nissim,Karen Weisinger,Casper Enghuus,Sebastian Palacios,Melissa R. Humphrey,Zhizhuo Zhang,Zhizhuo Zhang,Eva Maria Novoa,Eva Maria Novoa,Manolis Kellis,Manolis Kellis,Ron Weiss,Samuel D. Rabkin,Yuval Tabach,Timothy K. Lu +18 more
TL;DR: A next-generation sequencing approach combined with machine learning is used to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state.
Journal ArticleDOI
CladeOScope: functional interactions through the prism of clade-wise co-evolution.
Tomer Tsaban,Doron Stupp,Dana Sherill-Rofe,Idit Bloch,Elad Sharon,Ora Schueler-Furman,Reuven Wiener,Yuval Tabach +7 more
TL;DR: The CladeOScope method as discussed by the authors integrates information from different clades to reveal local co-evolution signals and improve function prediction in eukaryotic genomes, which is a powerful approach to uncover functional interactions between genes and to associate them with pathways.
Journal ArticleDOI
Co-evolution based machine-learning for predicting functional interactions between human genes.
TL;DR: In this article, a machine learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species was developed to predict functional interactions between human genes and the context for these interactions.
Journal ArticleDOI
Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals.
Idit Bloch,Dana Sherill-Rofe,Doron Stupp,Irene Unterman,Hodaya Beer,Elad Sharon,Yuval Tabach +6 more
TL;DR: A reliable and usable NPP construction pipeline is created using NPP from 1028 genomes, both separately and in various value combinations, and several parameter sets that optimized performance for pathways with certain biological annotation are identified.