His primary areas of study are Computational biology, Genetics, Gene, Bioinformatics and Metabolic network. His studies deal with areas such as Disease, Antibody, Tissue specific, Epitope mapping and Genome scale as well as Computational biology. His work investigates the relationship between Genetics and topics such as Theoretical computer science that intersect with problems in Function, Color-coding, Tree and Bounded function.
His research investigates the link between Gene and topics such as Protein–protein interaction that cross with problems in Function, Identification and Cross-validation. His Bioinformatics research incorporates elements of Cancer and Drug. His research integrates issues of Evolutionary biology, Ecology, Systems biology, Phylogenetics and Scale in his study of Metabolic network.
Eytan Ruppin spends much of his time researching Computational biology, Gene, Artificial intelligence, Genetics and Cancer research. As a part of the same scientific family, Eytan Ruppin mostly works in the field of Computational biology, focusing on Bioinformatics and, on occasion, Drug. His study connects Disease and Gene.
His Artificial intelligence study integrates concerns from other disciplines, such as Natural language processing, Machine learning and Pattern recognition. His work in Cancer research tackles topics such as Cancer which are related to areas like Synthetic lethality. The concepts of his Artificial neural network study are interwoven with issues in Function, Attractor and Neuroscience.
His primary scientific interests are in Cancer research, Cancer, Computational biology, Gene and Melanoma. His Cancer research study combines topics from a wide range of disciplines, such as Cell growth, Cancer cell, Suppressor and T cell, Immune system. His work in Cancer addresses subjects such as Synthetic lethality, which are connected to disciplines such as DNA methylation.
His Computational biology research is multidisciplinary, incorporating elements of CRISPR, Disease and Identification. His Gene study is concerned with the larger field of Genetics. Eytan Ruppin combines subjects such as Immune checkpoint, Regulator and Ubiquitin ligase with his study of Melanoma.
His primary areas of investigation include Cancer research, Immune checkpoint, Melanoma, Blockade and Cancer. The study incorporates disciplines such as Gene expression profiling, Cancer cell, Downregulation and upregulation, Polyglutamate and Nanocarriers in addition to Cancer research. The concepts of his Downregulation and upregulation study are interwoven with issues in Glutamine and Argininosuccinate synthase.
His work carried out in the field of Melanoma brings together such families of science as Immune system, Immunity and Cell biology. His study looks at the intersection of Blockade and topics like Metastatic melanoma with Published Erratum and Information retrieval. His Cancer study combines topics from a wide range of disciplines, such as Transcriptome, Diabetes mellitus genetics, Epidemiology and Bioinformatics.
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Placing search in context: the concept revisited.
Lev Finkelstein;Evgeniy Gabrilovich;Yossi Matias;Ehud Rivlin.
ACM Transactions on Information Systems (2002)
Associating Genes and Protein Complexes with Disease via Network Propagation
Oron Vanunu;Oded Magger;Eytan Ruppin;Tomer Shlomi.
PLOS Computational Biology (2010)
PREDICT: a method for inferring novel drug indications with application to personalized medicine.
Assaf Gottlieb;Gideon Y Stein;Gideon Y Stein;Eytan Ruppin;Roded Sharan.
Molecular Systems Biology (2011)
Network-based prediction of human tissue-specific metabolism
Tomer Shlomi;Moran N Cabili;Markus J Herrgård;Bernhard Ø Palsson.
Nature Biotechnology (2008)
Translation efficiency is determined by both codon bias and folding energy
Tamir Tuller;Yedael Y. Waldman;Martin Kupiec;Eytan Ruppin.
Proceedings of the National Academy of Sciences of the United States of America (2010)
Regulatory on/off minimization of metabolic flux changes after genetic perturbations
Tomer Shlomi;Omer Berkman;Eytan Ruppin.
Proceedings of the National Academy of Sciences of the United States of America (2005)
Predicting selective drug targets in cancer through metabolic networks
Ori Folger;Livnat Jerby;Christian Frezza;Eyal Gottlieb.
Molecular Systems Biology (2011)
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Daphna Joel;Yael Niv;Eytan Ruppin.
Neural Networks (2002)
Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase
Christian Frezza;Liang Zheng;Ori Folger;Kartik N. Rajagopalan.
Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism
Livnat Jerby;Tomer Shlomi;Eytan Ruppin.
Molecular Systems Biology (2010)
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