| Year |
Citation |
Score |
| 2020 |
Shaked MS, Dassa B, Sinha S, Di Agostino S, Azuri I, Mukherjee S, Aylon Y, Blandino G, Ruppin E, Oren M. A division of labor between YAP and TAZ in non-small cell lung cancer. Cancer Research. PMID 32816858 DOI: 10.1158/0008-5472.Can-20-0125 |
0.313 |
|
| 2020 |
Dattilo R, Mottini C, Camera E, Lamolinara A, Auslander N, Doglioni G, Muscolini M, Tang W, Planque M, Ercolani C, Buglioni S, Manni I, Trisciuoglio D, Boe A, Grande S, ... ... Ruppin E, et al. Pyrvinium pamoate induces death of triple-negative breast cancer stem-like cells and reduces metastases through effects on lipid anabolism. Cancer Research. PMID 32718996 DOI: 10.1158/0008-5472.Can-19-1184 |
0.323 |
|
| 2020 |
Erez A, Ruppin E, Keshet R, Lee JS. Abstract IA21: Blocking purine synthesis in cancer promotes response to immunotherapy Cancer Research. 80. DOI: 10.1158/1538-7445.Pedca19-Ia21 |
0.317 |
|
| 2020 |
Schischlik F, Lee JS, Shah N, Kaplan RN, Thiele CJ, Widemann B, Ruppin E. Abstract A46: Charting the synthetic lethality landscape in pediatric cancer to advance whole-exome precision-based treatments Cancer Research. 80. DOI: 10.1158/1538-7445.Pedca19-A46 |
0.383 |
|
| 2019 |
Katzir R, Polat IH, Harel M, Katz S, Foguet C, Selivanov VA, Sabatier P, Cascante M, Geiger T, Ruppin E. The landscape of tiered regulation of breast cancer cell metabolism. Scientific Reports. 9: 17760. PMID 31780802 DOI: 10.1038/S41598-019-54221-Y |
0.357 |
|
| 2019 |
Pathria G, Lee JS, Hasnis E, Tandoc K, Scott DA, Verma S, Feng Y, Larue L, Sahu AD, Topisirovic I, Ruppin E, Ronai ZA. Translational reprogramming marks adaptation to asparagine restriction in cancer. Nature Cell Biology. PMID 31740775 DOI: 10.1038/S41556-019-0415-1 |
0.312 |
|
| 2019 |
Kwon SM, Budhu A, Woo HG, Chaisaingmongkol J, Dang H, Forgues M, Harris CC, Zhang G, Auslander N, Ruppin E, Mahidol C, Ruchirawat M, Wang XW. Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer. Scientific Reports. 9: 16930. PMID 31729408 DOI: 10.1038/S41598-019-52578-8 |
0.303 |
|
| 2019 |
Nair NU, Das A, Rogkoti VM, Fokkelman M, Marcotte R, de Jong CG, Koedoot E, Lee JS, Meilijson I, Hannenhalli S, Neel BG, de Water BV, Le Dévédec SE, Ruppin E. Migration rather than proliferation transcriptomic signatures are strongly associated with breast cancer patient survival. Scientific Reports. 9: 10989. PMID 31358840 DOI: 10.1038/S41598-019-47440-W |
0.308 |
|
| 2019 |
Magen A, Das Sahu A, Lee JS, Sharmin M, Lugo A, Gutkind JS, Schäffer AA, Ruppin E, Hannenhalli S. Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer. Cell Reports. 28: 938-948.e6. PMID 31340155 DOI: 10.1016/J.Celrep.2019.06.067 |
0.374 |
|
| 2019 |
Sahu AD, S Lee J, Wang Z, Zhang G, Iglesias-Bartolome R, Tian T, Wei Z, Miao B, Nair NU, Ponomarova O, Friedman AA, Amzallag A, Moll T, Kasumova G, Greninger P, ... ... Ruppin E, et al. Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy. Molecular Systems Biology. 15: e8323. PMID 30858180 DOI: 10.15252/Msb.20188323 |
0.341 |
|
| 2019 |
Feng X, Arang N, Rigiracciolo DC, Lee JS, Yeerna H, Wang Z, Lubrano S, Kishore A, Pachter JA, König GM, Maggiolini M, Kostenis E, Schlaepfer DD, Tamayo P, Chen Q, ... Ruppin E, et al. A Platform of Synthetic Lethal Gene Interaction Networks Reveals that the GNAQ Uveal Melanoma Oncogene Controls the Hippo Pathway through FAK. Cancer Cell. PMID 30773340 DOI: 10.1016/J.Ccell.2019.01.009 |
0.314 |
|
| 2019 |
Das A, Lee JS, Zhang G, Wang Z, Amzallag A, Boland G, Hannenhalli S, Herlyn M, Benes C, Gutkind JS, Flaherty K, Ruppin E. Abstract LB-149: Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy Cancer Research. 79. DOI: 10.1158/1538-7445.Am2019-Lb-149 |
0.353 |
|
| 2018 |
Silberman A, Goldman O, Boukobza Assayag O, Jacob A, Limanovich S, Adler L, Lee JS, Keshet R, Sarver A, Frug J, Stettner N, Galai S, Persi E, Bahar Halpern K, Zaltsman-Amir Y, ... ... Ruppin E, et al. Acid-induced downregulation of ASS1 contributes to the maintenance of intracellular pH in cancer. Cancer Research. PMID 30573518 DOI: 10.1158/0008-5472.Can-18-1062 |
0.313 |
|
| 2018 |
Tang W, Zhou M, Dorsey TH, Prieto DA, Wang XW, Ruppin E, Veenstra TD, Ambs S. Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival. Genome Medicine. 10: 94. PMID 30501643 DOI: 10.1186/S13073-018-0602-X |
0.318 |
|
| 2018 |
Pathria G, Scott DA, Feng Y, Sang Lee J, Fujita Y, Zhang G, Sahu AD, Ruppin E, Herlyn M, Osterman AL, Ronai ZA. Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival. The Embo Journal. PMID 30209241 DOI: 10.15252/Embj.201899735 |
0.341 |
|
| 2018 |
Lee JS, Adler L, Karathia H, Carmel N, Rabinovich S, Auslander N, Keshet R, Stettner N, Silberman A, Agemy L, Helbling D, Eilam R, Sun Q, Brandis A, Malitsky S, ... ... Ruppin E, et al. Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures. Cell. PMID 30100185 DOI: 10.1016/J.Cell.2018.07.019 |
0.305 |
|
| 2018 |
Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N, Davidson M, McGarry L, James D, Amzallag A, Park SG, Cheng K, Robinson W, Atias D, Stossel C, Buzhor E, ... ... Ruppin E, et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nature Communications. 9: 2546. PMID 29959327 DOI: 10.1038/S41467-018-04647-1 |
0.327 |
|
| 2018 |
Tiram G, Ferber S, Ofek P, Eldar-Boock A, Ben-Shushan D, Yeini E, Krivitsky A, Blatt R, Almog N, Henkin J, Amsalem O, Yavin E, Cohen G, Lazarovici P, Lee JS, ... Ruppin E, et al. Reverting the molecular fingerprint of tumor dormancy as a therapeutic strategy for glioblastoma. Faseb Journal : Official Publication of the Federation of American Societies For Experimental Biology. fj201701568R. PMID 29856660 DOI: 10.1096/Fj.201701568R |
0.305 |
|
| 2018 |
Nair NU, Das A, Amit U, Robinson W, Park SG, Basu M, Lugo A, Leor J, Ruppin E, Hannenhalli S. Putative functional genes in idiopathic dilated cardiomyopathy. Scientific Reports. 8: 66. PMID 29311597 DOI: 10.1038/S41598-017-18524-2 |
0.325 |
|
| 2018 |
Lee JS, Carmel N, Karathia H, Auslander N, Rabinovich S, Keshet R, Stettner N, Silberman A, Agemy L, Helbling D, Eilam R, Sun Q, Brandis A, Weiss H, Dimmock D, ... ... Ruppin E, et al. Abstract A69: Mutagenicity of urea cycle dysregulation and its implications for cancer immunotherapy Cancer Immunology Research. 6. DOI: 10.1158/2326-6074.Tumimm17-A69 |
0.349 |
|
| 2018 |
Feng X, Rigiracciolo D, Lee J, Yeerna H, Arang N, Lubrano S, Schlaepfer DD, Tamayo P, Ruppin E, Gutkind JS. Abstract 968: Targeting FAK inhibits YAP-dependent tumor growth in uveal melanoma Cancer Research. 78: 968-968. DOI: 10.1158/1538-7445.Am2018-968 |
0.319 |
|
| 2018 |
Lee JS, Das A, Jerby-Arnon L, Arafeh R, Davidson M, Amzallag A, Park SG, Cheng K, Robinson W, Atias D, Stossel C, Buzhor E, Stein G, Waterfall JJ, Meltzer PS, ... ... Ruppin E, et al. Abstract A188: Harnessing synthetic lethality to predict the response to cancer treatments Molecular Cancer Therapeutics. 17. DOI: 10.1158/1535-7163.Targ-17-A188 |
0.376 |
|
| 2018 |
Nair NU, Das A, Lee JS, Hannenhalli S, Dévédec SL, Water Bvd, Ruppin E. Abstract A023: Cell migration is a stronger predictor of patient survival in breast cancer than cell proliferation Molecular Cancer Therapeutics. 17. DOI: 10.1158/1535-7163.Targ-17-A023 |
0.334 |
|
| 2017 |
Auslander N, Cunningham CE, Toosi BM, McEwen EJ, Yizhak K, Vizeacoumar FS, Parameswaran S, Gonen N, Freywald T, Bhanumathy KK, Freywald A, Vizeacoumar FJ, Ruppin E. An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer. Molecular Systems Biology. 13: 956-956. PMID 29196508 DOI: 10.15252/Msb.20177739 |
0.375 |
|
| 2017 |
Senft D, Leiserson MDM, Ruppin E, Ronai ZA. Precision Oncology: The Road Ahead. Trends in Molecular Medicine. PMID 28887051 DOI: 10.1016/J.Molmed.2017.08.003 |
0.314 |
|
| 2017 |
Duran-Frigola M, Siragusa L, Ruppin E, Barril X, Cruciani G, Aloy P. Detecting similar binding pockets to enable systems polypharmacology. Plos Computational Biology. 13: e1005522. PMID 28662117 DOI: 10.1371/Journal.Pcbi.1005522 |
0.303 |
|
| 2017 |
Lee JS, Das A, Jerby-Arnon L, Atias D, Amzallag A, Benes CH, Golan T, Ruppin E. Abstract PR09: Harnessing synthetic lethality to predict clinical outcomes of cancer treatment Molecular Cancer Therapeutics. 16. DOI: 10.1158/1538-8514.Synthleth-Pr09 |
0.377 |
|
| 2017 |
Magen A, Das A, Lee J, Hannenhalli S, Ruppin E. Abstract 1558: Data-driven approach to detecting novel gene interactions in cancer with applications to drug response prediction and cancer stratification Cancer Research. 77: 1558-1558. DOI: 10.1158/1538-7445.Am2017-1558 |
0.332 |
|
| 2016 |
Cohen O, Oberhardt M, Yizhak K, Ruppin E. Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy. Plos One. 11: e0168444. PMID 27997585 DOI: 10.1371/Journal.Pone.0168444 |
0.329 |
|
| 2016 |
Auslander N, Wagner A, Oberhardt M, Ruppin E. Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction. Plos Computational Biology. 12: e1005125. PMID 27673682 DOI: 10.1371/Journal.Pcbi.1005125 |
0.371 |
|
| 2016 |
Cunningham CE, Li S, Vizeacoumar FS, Bhanumathy KK, Lee JS, Parameswaran S, Furber L, Abuhussein O, Paul JM, McDonald M, Templeton SD, Shukla H, El Zawily AM, Boyd F, Alli N, ... ... Ruppin E, et al. Therapeutic relevance of the protein phosphatase 2A in cancer. Oncotarget. PMID 27557495 DOI: 10.18632/Oncotarget.11399 |
0.34 |
|
| 2016 |
Mokryn O, Wagner A, Blattner M, Ruppin E, Shavitt Y. The Role of Temporal Trends in Growing Networks. Plos One. 11: e0156505. PMID 27486847 DOI: 10.1371/Journal.Pone.0156505 |
0.31 |
|
| 2016 |
Auslander N, Yizhak K, Weinstock A, Budhu A, Tang W, Wang XW, Ambs S, Ruppin E. A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer. Scientific Reports. 6: 29662. PMID 27406679 DOI: 10.1038/Srep29662 |
0.332 |
|
| 2016 |
Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E. Metabolic Network Prediction of Drug Side Effects. Cell Systems. 2: 209-13. PMID 27135366 DOI: 10.1016/J.Cels.2016.03.001 |
0.325 |
|
| 2016 |
Oberhardt MA, Zarecki R, Reshef L, Xia F, Duran-Frigola M, Schreiber R, Henry CS, Ben-Tal N, Dwyer DJ, Gophna U, Ruppin E. Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli. Plos Computational Biology. 12: e1004705. PMID 26821166 DOI: 10.1371/Journal.Pcbi.1004705 |
0.36 |
|
| 2016 |
Mazza A, Wagner A, Ruppin E, Sharan R. Functional Alignment of Metabolic Networks. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. PMID 26759932 DOI: 10.1089/Cmb.2015.0203 |
0.358 |
|
| 2016 |
Rabinovich S, Yizhak K, Sun Q, Brandis A, Helbling D, Dimmock D, Nagamani S, Ruppin E, Erez A. Abstract PR05: Aspartate metabolism links the urea cycle with nucleic acid synthesis in cancerous proliferation Molecular Cancer Research. 14. DOI: 10.1158/1557-3125.Metca15-Pr05 |
0.327 |
|
| 2015 |
Jerby-Arnon L, Ruppin E. Moving ahead on harnessing synthetic lethality to fight cancer. Molecular and Cellular Oncology. 2. PMID 27308440 DOI: 10.4161/23723556.2014.977150 |
0.372 |
|
| 2015 |
Rabinovich S, Adler L, Yizhak K, Sarver A, Silberman A, Agron S, Stettner N, Sun Q, Brandis A, Helbling D, Korman S, Itzkovitz S, Dimmock D, Ulitsky I, Nagamani SC, ... Ruppin E, et al. Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis. Nature. PMID 26560030 DOI: 10.1038/Nature15529 |
0.32 |
|
| 2015 |
Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proceedings of the National Academy of Sciences of the United States of America. 112: 12217-22. PMID 26371301 DOI: 10.1073/Pnas.1508573112 |
0.408 |
|
| 2015 |
Wagner A, Cohen N, Kelder T, Amit U, Liebman E, Steinberg DM, Radonjic M, Ruppin E. Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia. Molecular Systems Biology. 11: 791. PMID 26148350 DOI: 10.15252/Msb.20145486 |
0.319 |
|
| 2015 |
Yizhak K, Chaneton B, Gottlieb E, Ruppin E. Modeling cancer metabolism on a genome scale. Molecular Systems Biology. 11: 817. PMID 26130389 DOI: 10.15252/Msb.20145307 |
0.391 |
|
| 2015 |
Ish-Am O, Kristensen DM, Ruppin E. Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media. Plos One. 10: e0123785. PMID 25894004 DOI: 10.1371/Journal.Pone.0123785 |
0.336 |
|
| 2015 |
Seaver SM, Bradbury LM, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. Frontiers in Plant Science. 6: 142. PMID 25806041 DOI: 10.3389/Fpls.2015.00142 |
0.35 |
|
| 2015 |
Patella F, Schug ZT, Persi E, Neilson LJ, Erami Z, Avanzato D, Maione F, Hernandez-Fernaud JR, Mackay G, Zheng L, Reid S, Frezza C, Giraudo E, Fiorio Pla A, Anderson K, ... Ruppin E, et al. Proteomics-based metabolic modeling reveals that fatty acid oxidation (FAO) controls endothelial cell (EC) permeability. Molecular & Cellular Proteomics : McP. 14: 621-34. PMID 25573745 DOI: 10.1074/Mcp.M114.045575 |
0.3 |
|
| 2015 |
Uziel O, Yosef N, Sharan R, Ruppin E, Kupiec M, Kushnir M, Beery E, Cohen-Diker T, Nordenberg J, Lahav M. The effects of telomere shortening on cancer cells: a network model of proteomic and microRNA analysis. Genomics. 105: 5-16. PMID 25451739 DOI: 10.1016/J.Ygeno.2014.10.013 |
0.327 |
|
| 2015 |
Patella F, Schug ZT, Persi E, Neilson LJ, Erami Z, Avanzato D, Maione F, Hernandez-Fernaud JR, Mackay G, Zheng L, Reid S, Frezza C, Giraudo E, Pla AF, Anderson K, ... Ruppin E, et al. Abstract B17: In-depth proteomics unveils fatty acid oxidation role in controlling vascular permeability Molecular Cancer Therapeutics. 14. DOI: 10.1158/1538-8514.Tumang15-B17 |
0.334 |
|
| 2015 |
Sahu AD, Lee JS, Hannenhalli S, Ruppin E. Abstract B56: Tracing synthetic rescue reprogramming to counteract cancer resistance Molecular Cancer Therapeutics. 14. DOI: 10.1158/1535-7163.Targ-15-B56 |
0.383 |
|
| 2014 |
Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. Elife. 3. PMID 25415239 DOI: 10.7554/Elife.03641 |
0.358 |
|
| 2014 |
Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, Seashore-Ludlow B, Weinstock A, Geiger T, Clemons PA, Gottlieb E, Ruppin E. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell. 158: 1199-209. PMID 25171417 DOI: 10.1016/J.Cell.2014.07.027 |
0.383 |
|
| 2014 |
Stempler S, Yizhak K, Ruppin E. Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease. Plos One. 9: e105383. PMID 25127241 DOI: 10.1371/Journal.Pone.0105383 |
0.325 |
|
| 2014 |
Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Molecular Systems Biology. 10: 744. PMID 25086087 DOI: 10.15252/Msb.20134993 |
0.36 |
|
| 2014 |
Notebaart RA, Szappanos B, Kintses B, Pál F, Györkei Ã, Bogos B, Lázár V, Spohn R, CsörgÅ‘ B, Wagner A, Ruppin E, Pál C, Papp B. Network-level architecture and the evolutionary potential of underground metabolism. Proceedings of the National Academy of Sciences of the United States of America. 111: 11762-7. PMID 25071190 DOI: 10.1073/Pnas.1406102111 |
0.369 |
|
| 2014 |
Zarecki R, Oberhardt MA, Reshef L, Gophna U, Ruppin E. A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness. Plos Computational Biology. 10: e1003726. PMID 25033033 DOI: 10.1371/Journal.Pcbi.1003726 |
0.308 |
|
| 2014 |
Zarecki R, Oberhardt MA, Yizhak K, Wagner A, Shtifman Segal E, Freilich S, Henry CS, Gophna U, Ruppin E. Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms. Plos One. 9: e98372. PMID 24866123 DOI: 10.1371/Journal.Pone.0098372 |
0.314 |
|
| 2014 |
Yizhak K, Gaude E, Dévédec SL, Waldman YY, Stein GY, Water Bvd, Frezza C, Ruppin E. Author response: Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer Elife. DOI: 10.7554/Elife.03641.023 |
0.333 |
|
| 2013 |
Goldstein I, Yizhak K, Madar S, Goldfinger N, Ruppin E, Rotter V. p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production. Cancer & Metabolism. 1: 9. PMID 24280180 DOI: 10.1186/2049-3002-1-9 |
0.313 |
|
| 2013 |
Wagner A, Zarecki R, Reshef L, Gochev C, Sorek R, Gophna U, Ruppin E. Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious. Proceedings of the National Academy of Sciences of the United States of America. 110: 19166-71. PMID 24198337 DOI: 10.1073/Pnas.1312361110 |
0.378 |
|
| 2013 |
Yizhak K, Gabay O, Cohen H, Ruppin E. Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nature Communications. 4: 2632. PMID 24153335 DOI: 10.1038/Ncomms3632 |
0.363 |
|
| 2013 |
Waldman YY, Geiger T, Ruppin E. A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells. Plos Genetics. 9: e1003806. PMID 24068970 DOI: 10.1371/Journal.Pgen.1003806 |
0.342 |
|
| 2013 |
Arnon LJ, Weinstock A, Geiger T, Ruppin E. Abstract A32: Systematic reconstruction of the cancer synthetic lethal network and its application for the identification of selective cancer drug targets Molecular Cancer Therapeutics. 12. DOI: 10.1158/1535-7163.Pms-A32 |
0.373 |
|
| 2012 |
Jerby L, Ruppin E. Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clinical Cancer Research : An Official Journal of the American Association For Cancer Research. 18: 5572-84. PMID 23071359 DOI: 10.1158/1078-0432.Ccr-12-1856 |
0.373 |
|
| 2012 |
Stein GY, Yosef N, Reichman H, Horev J, Laser-Azogui A, Berens A, Resau J, Ruppin E, Sharan R, Tsarfaty I. Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival. Plos One. 7: e45969. PMID 23049908 DOI: 10.1371/Journal.Pone.0045969 |
0.36 |
|
| 2012 |
Stempler S, Ruppin E. Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer's disease. Plos One. 7: e45879. PMID 23029292 DOI: 10.1371/Journal.Pone.0045879 |
0.314 |
|
| 2012 |
Magger O, Waldman YY, Ruppin E, Sharan R. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. Plos Computational Biology. 8: e1002690. PMID 23028288 DOI: 10.1371/Journal.Pcbi.1002690 |
0.336 |
|
| 2012 |
Jerby L, Wolf L, Denkert C, Stein GY, Hilvo M, Oresic M, Geiger T, Ruppin E. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Research. 72: 5712-20. PMID 22986741 DOI: 10.1158/0008-5472.Can-12-2215 |
0.365 |
|
| 2012 |
Lobel L, Sigal N, Borovok I, Ruppin E, Herskovits AA. Integrative genomic analysis identifies isoleucine and CodY as regulators of Listeria monocytogenes virulence. Plos Genetics. 8: e1002887. PMID 22969433 DOI: 10.1371/Journal.Pgen.1002887 |
0.335 |
|
| 2012 |
Stempler S, Waldman YY, Wolf L, Ruppin E. Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression. Neurobiology of Aging. 33: 2230.e13-2230.e21. PMID 22560482 DOI: 10.1016/J.Neurobiolaging.2012.04.003 |
0.332 |
|
| 2012 |
Ben-Shitrit T, Yosef N, Shemesh K, Sharan R, Ruppin E, Kupiec M. Systematic identification of gene annotation errors in the widely used yeast mutation collections. Nature Methods. 9: 373-8. PMID 22306811 DOI: 10.1038/Nmeth.1890 |
0.301 |
|
| 2012 |
Vardi L, Ruppin E, Sharan R. A linearized constraint-based approach for modeling signaling networks. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 19: 232-40. PMID 22300322 DOI: 10.1089/Cmb.2011.0277 |
0.319 |
|
| 2012 |
Mintz-Oron S, Meir S, Malitsky S, Ruppin E, Aharoni A, Shlomi T. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proceedings of the National Academy of Sciences of the United States of America. 109: 339-44. PMID 22184215 DOI: 10.1073/Pnas.1100358109 |
0.316 |
|
| 2012 |
Yizhak K, Gabay O, Cohen H, Ruppin E. Metabolic modeling predicts perturbations extending lifespan in yeast and counteracting aging in mammalian muscle Bmc Proceedings. 6: 54. DOI: 10.1186/1753-6561-6-S3-P54 |
0.349 |
|
| 2011 |
Yosef N, Zalckvar E, Rubinstein AD, Homilius M, Atias N, Vardi L, Berman I, Zur H, Kimchi A, Ruppin E, Sharan R. ANAT: a tool for constructing and analyzing functional protein networks. Science Signaling. 4: pl1. PMID 22028466 DOI: 10.1126/Scisignal.2001935 |
0.342 |
|
| 2011 |
Gottlieb A, Magger O, Berman I, Ruppin E, Sharan R. PRINCIPLE: a tool for associating genes with diseases via network propagation. Bioinformatics (Oxford, England). 27: 3325-6. PMID 22016407 DOI: 10.1093/Bioinformatics/Btr584 |
0.308 |
|
| 2011 |
Reuveni S, Meilijson I, Kupiec M, Ruppin E, Tuller T. Genome-scale analysis of translation elongation with a ribosome flow model. Plos Computational Biology. 7: e1002127. PMID 21909250 DOI: 10.1371/Journal.Pcbi.1002127 |
0.334 |
|
| 2011 |
Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, Kalna G, Tomlinson IP, Pollard PJ, Watson DG, Deberardinis RJ, ... ... Ruppin E, et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature. 477: 225-8. PMID 21849978 DOI: 10.1038/Nature10363 |
0.329 |
|
| 2011 |
Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Molecular Systems Biology. 7: 501. PMID 21694718 DOI: 10.1038/Msb.2011.35 |
0.387 |
|
| 2011 |
Yizhak K, Tuller T, Papp B, Ruppin E. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Molecular Systems Biology. 7: 479. PMID 21451589 DOI: 10.1038/Msb.2011.11 |
0.382 |
|
| 2011 |
Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. Plos Computational Biology. 7: e1002018. PMID 21423717 DOI: 10.1371/Journal.Pcbi.1002018 |
0.362 |
|
| 2011 |
Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks Nature. 7. DOI: 10.1038/Msb.2011.51 |
0.353 |
|
| 2010 |
Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics (Oxford, England). 26: 3140-2. PMID 21081510 DOI: 10.1093/Bioinformatics/Btq602 |
0.329 |
|
| 2010 |
Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Molecular Systems Biology. 6: 401. PMID 20823844 DOI: 10.1038/Msb.2010.56 |
0.331 |
|
| 2010 |
Ruppin E, Papin JA, de Figueiredo LF, Schuster S. Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. Current Opinion in Biotechnology. 21: 502-10. PMID 20692823 DOI: 10.1016/J.Copbio.2010.07.002 |
0.347 |
|
| 2010 |
Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics (Oxford, England). 26: i255-60. PMID 20529914 DOI: 10.1093/Bioinformatics/Btq183 |
0.351 |
|
| 2010 |
Benyamini T, Folger O, Ruppin E, Shlomi T. Flux balance analysis accounting for metabolite dilution. Genome Biology. 11: R43. PMID 20398381 DOI: 10.1186/Gb-2010-11-4-R43 |
0.323 |
|
| 2010 |
Freilich S, Kreimer A, Borenstein E, Gophna U, Sharan R, Ruppin E. Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species. Plos Computational Biology. 6: e1000690. PMID 20195496 DOI: 10.1371/Journal.Pcbi.1000690 |
0.32 |
|
| 2010 |
Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R. Associating genes and protein complexes with disease via network propagation. Plos Computational Biology. 6: e1000641. PMID 20090828 DOI: 10.1371/Journal.Pcbi.1000641 |
0.342 |
|
| 2010 |
Peleg T, Yosef N, Ruppin E, Sharan R. Network-free inference of knockout effects in yeast. Plos Computational Biology. 6: e1000635. PMID 20066032 DOI: 10.1371/Journal.Pcbi.1000635 |
0.365 |
|
| 2009 |
Tuller T, Ruppin E, Kupiec M. Properties of untranslated regions of the S. cerevisiae genome. Bmc Genomics. 10: 391. PMID 19698117 DOI: 10.1186/1471-2164-10-391 |
0.32 |
|
| 2009 |
Diamant I, Eldar YC, Rokhlenko O, Ruppin E, Shlomi T. A network-based method for predicting gene-nutrient interactions and its application to yeast amino-acid metabolism. Molecular Biosystems. 5: 1732-9. PMID 19593469 DOI: 10.1039/B823287N |
0.365 |
|
| 2009 |
Freilich S, Kreimer A, Borenstein E, Yosef N, Sharan R, Gophna U, Ruppin E. Metabolic-network-driven analysis of bacterial ecological strategies. Genome Biology. 10: R61. PMID 19500338 DOI: 10.1186/Gb-2009-10-6-R61 |
0.321 |
|
| 2009 |
Mintz-Oron S, Aharoni A, Ruppin E, Shlomi T. Network-based prediction of metabolic enzymes' subcellular localization. Bioinformatics (Oxford, England). 25: i247-52. PMID 19477995 DOI: 10.1093/Bioinformatics/Btp209 |
0.314 |
|
| 2009 |
Tuller T, Kupiec M, Ruppin E. Co-evolutionary networks of genes and cellular processes across fungal species. Genome Biology. 10: R48. PMID 19416514 DOI: 10.1186/Gb-2009-10-5-R48 |
0.324 |
|
| 2009 |
Shlomi T, Cabili MN, Ruppin E. Predicting metabolic biomarkers of human inborn errors of metabolism. Molecular Systems Biology. 5: 263. PMID 19401675 DOI: 10.1038/Msb.2009.22 |
0.349 |
|
| 2009 |
Yosef N, Ungar L, Zalckvar E, Kimchi A, Kupiec M, Ruppin E, Sharan R. Toward accurate reconstruction of functional protein networks. Molecular Systems Biology. 5: 248. PMID 19293828 DOI: 10.1038/Msb.2009.3 |
0.353 |
|
| 2008 |
Borenstein E, Kupiec M, Feldman MW, Ruppin E. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proceedings of the National Academy of Sciences of the United States of America. 105: 14482-7. PMID 18787117 DOI: 10.1073/Pnas.0806162105 |
0.316 |
|
| 2008 |
Shlomi T, Cabili MN, Herrgård MJ, Palsson BØ, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nature Biotechnology. 26: 1003-10. PMID 18711341 DOI: 10.1038/Nbt.1487 |
0.333 |
|
| 2008 |
Dost B, Shlomi T, Gupta N, Ruppin E, Bafna V, Sharan R. QNet: a tool for querying protein interaction networks. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 15: 913-25. PMID 18707533 DOI: 10.1089/Cmb.2007.0172 |
0.313 |
|
| 2008 |
Deutscher D, Meilijson I, Schuster S, Ruppin E. Can single knockouts accurately single out gene functions? Bmc Systems Biology. 2: 50. PMID 18564419 DOI: 10.1186/1752-0509-2-50 |
0.357 |
|
| 2008 |
Kreimer A, Borenstein E, Gophna U, Ruppin E. The evolution of modularity in bacterial metabolic networks. Proceedings of the National Academy of Sciences of the United States of America. 105: 6976-81. PMID 18460604 DOI: 10.1073/Pnas.0712149105 |
0.345 |
|
| 2008 |
Shachar R, Ungar L, Kupiec M, Ruppin E, Sharan R. A systems-level approach to mapping the telomere length maintenance gene circuitry. Molecular Systems Biology. 4: 172. PMID 18319724 DOI: 10.1038/Msb.2008.13 |
0.341 |
|
| 2008 |
Behre J, Wilhelm T, von Kamp A, Ruppin E, Schuster S. Structural robustness of metabolic networks with respect to multiple knockouts. Journal of Theoretical Biology. 252: 433-41. PMID 18023456 DOI: 10.1016/J.Jtbi.2007.09.043 |
0.339 |
|
| 2007 |
Shlomi T, Herrgard M, Portnoy V, Naim E, Palsson BØ, Sharan R, Ruppin E. Systematic condition-dependent annotation of metabolic genes. Genome Research. 17: 1626-33. PMID 17895423 DOI: 10.1101/Gr.6678707 |
0.349 |
|
| 2007 |
Rokhlenko O, Shlomi T, Sharan R, Ruppin E, Pinter RY. Constraint-based functional similarity of metabolic genes: going beyond network topology. Bioinformatics (Oxford, England). 23: 2139-46. PMID 17586548 DOI: 10.1093/Bioinformatics/Btm319 |
0.366 |
|
| 2007 |
Fishel I, Kaufman A, Ruppin E. Meta-analysis of gene expression data Bioinformatics. 23: 1599-1606. PMID 17463023 DOI: 10.1093/Bioinformatics/Btm149 |
0.324 |
|
| 2007 |
Shlomi T, Eisenberg Y, Sharan R, Ruppin E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Molecular Systems Biology. 3: 101. PMID 17437026 DOI: 10.1038/Msb4100141 |
0.354 |
|
| 2007 |
Meshi O, Shlomi T, Ruppin E. Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network. Bmc Systems Biology. 1: 1-7. PMID 17408505 DOI: 10.1186/1752-0509-1-1 |
0.305 |
|
| 2007 |
Kupiec M, Sharan R, Ruppin E. Genetic interactions in yeast: is robustness going bust? Molecular Systems Biology. 3: 97. PMID 17389877 DOI: 10.1038/Msb4100146 |
0.314 |
|
| 2007 |
Yosef N, Yakhini Z, Tsalenko A, Kristensen V, Børresen-Dale AL, Ruppin E, Sharan R. A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data. Bioinformatics (Oxford, England). 23: e91-8. PMID 17237111 DOI: 10.1093/Bioinformatics/Btl298 |
0.324 |
|
| 2007 |
Borenstein E, Shlomi T, Ruppin E, Sharan R. Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes. Nucleic Acids Research. 35: e7. PMID 17158152 DOI: 10.1093/Nar/Gkl792 |
0.331 |
|
| 2006 |
Kaufman A, Dror G, Meilijson I, Ruppin E. Gene Expression of Caenorhabditis elegans Neurons Carries Information on Their Synaptic Connectivity Plos Computational Biology. 2. PMID 17154715 DOI: 10.1371/Journal.Pcbi.0020167 |
0.329 |
|
| 2006 |
Deutscher D, Meilijson I, Kupiec M, Ruppin E. Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nature Genetics. 38: 993-8. PMID 16941010 DOI: 10.1038/Ng1856 |
0.356 |
|
| 2006 |
Bilu Y, Shlomi T, Barkai N, Ruppin E. Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states. Plos Computational Biology. 2: e106. PMID 16933982 DOI: 10.1371/Journal.Pcbi.0020106 |
0.349 |
|
| 2006 |
Yosef N, Kaufman A, Ruppin E. Inferring functional pathways from multi-perturbation data. Bioinformatics (Oxford, England). 22: e539-46. PMID 16873518 DOI: 10.1093/Bioinformatics/Btl204 |
0.347 |
|
| 2006 |
Keinan A, Sandbank B, Hilgetag CC, Meilijson I, Ruppin E. Axiomatic scalable neurocontroller analysis via the Shapley value. Artificial Life. 12: 333-52. PMID 16859444 DOI: 10.1162/Artl.2006.12.3.333 |
0.324 |
|
| 2006 |
Shlomi T, Segal D, Ruppin E, Sharan R. QPath: A method for querying pathways in a protein-protein interaction network Bmc Bioinformatics. 7. PMID 16606460 DOI: 10.1186/1471-2105-7-199 |
0.321 |
|
| 2006 |
Saggie-Wexler K, Keinan A, Ruppin E. Neural Processing of Counting in Evolved Spiking and McCulloch-Pitts Agents Artificial Life. 12: 1-16. PMID 16393448 DOI: 10.1162/106454606775186428 |
0.309 |
|
| 2005 |
Kaufman A, Keinan A, Meilijson I, Kupiec M, Ruppin E. Quantitative analysis of genetic and neuronal multi-perturbation experiments. Plos Computational Biology. 1: e64. PMID 16322764 DOI: 10.1371/Journal.Pcbi.0010064 |
0.309 |
|
| 2005 |
Shlomi T, Berkman O, Ruppin E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations Proceedings of the National Academy of Sciences of the United States of America. 102: 7695-7700. PMID 15897462 DOI: 10.1073/Pnas.0406346102 |
0.329 |
|
| 2004 |
Keinan A, Sandbank B, Hilgetag CC, Meilijson I, Ruppin E. Fair attribution of functional contribution in artificial and biological networks. Neural Computation. 16: 1887-915. PMID 15265327 DOI: 10.1162/0899766041336387 |
0.326 |
|
| 2004 |
Saggie K, Keinan A, Ruppin E. Spikes that count: rethinking spikiness in neurally embedded systems Neurocomputing. 58: 303-311. DOI: 10.1016/J.Neucom.2004.01.060 |
0.312 |
|
| 2004 |
Keinan A, Hilgetag CC, Meilijson I, Ruppin E. Causal localization of neural function: The Shapley value method Neurocomputing. 58: 215-222. DOI: 10.1016/J.Neucom.2004.01.046 |
0.303 |
|
| 2003 |
Boshy S, Ruppin E. Evolving small neurocontrollers with self-organized compact encoding Artificial Life. 9: 131-151. PMID 12906726 DOI: 10.1162/106454603322221496 |
0.311 |
|
| 2003 |
Segev L, Aharonov R, Meilijson I, Ruppin E. High-dimensional analysis of evolutionary autonomous agents. Artificial Life. 9: 1-20. PMID 12725679 DOI: 10.1162/106454603321489491 |
0.314 |
|
| 2003 |
Aharonov R, Segev L, Meilijson I, Ruppin E. Localization of function via lesion analysis. Neural Computation. 15: 885-913. PMID 12689391 DOI: 10.1162/08997660360581949 |
0.318 |
|
| 2001 |
Aharonov-barki R, Beker T, Ruppin E. Emergence of Memory-Driven Command Neurons in Evolved Artificial Agents Neural Computation. 13: 691-716. PMID 11244562 DOI: 10.1162/089976601300014529 |
0.3 |
|
| 1996 |
Meilijson I, Ruppin E. Optimal firing in sparsely-connected low-activity attractor networks Biological Cybernetics. 74: 479-485. DOI: 10.1007/Bf00209419 |
0.3 |
|
| 1995 |
Meilijson I, Ruppin E, Sipper M. A single-iteration threshold Hamming network Ieee Transactions On Neural Networks. 6: 261-266. PMID 18263307 DOI: 10.1109/72.363428 |
0.305 |
|
| 1994 |
Meilijson I, Ruppin E. Optimal signalling in attractor neural networks Network: Computation in Neural Systems. 5: 277-298. DOI: 10.1088/0954-898X_5_2_010 |
0.305 |
|
| 1993 |
Herrmann M, Ruppin E, Usher M. A neural model of the dynamic activation of memory Biological Cybernetics. 68: 455-463. PMID 8476986 DOI: 10.1007/Bf00198778 |
0.304 |
|
| 1993 |
Meilijson I, Ruppin E. History-dependent attractor neural networks Network: Computation in Neural Systems. 4: 195-221. DOI: 10.1088/0954-898X_4_2_004 |
0.32 |
|
| 1990 |
Ruppin E, Usher M. An attractor neural network model of semantic fact retrieval Network: Computation in Neural Systems. 1: 325-344. DOI: 10.1088/0954-898X_1_3_003 |
0.303 |
|
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