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Adam A. Margolin, Ph.D. - Publications

Affiliations: 
Oregon Health and Science University, Portland, OR 
Area:
Cancer, Gene regulatory models, Computational Biology
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60 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2018 Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, et al. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. PMID 30559381 DOI: 10.1038/S41586-018-0722-X  0.378
2018 Xu C, Nikolova O, Basom R, Mitchell RM, Shaw R, Moser R, Park H, Gurley KE, Kao M, Green CL, Schaub FX, Diaz RL, Swan HA, Jang IS, Guinney J, ... ... Margolin AA, et al. Functional precision medicine identifies novel druggable targets and therapeutic options in head and neck cancer. Clinical Cancer Research : An Official Journal of the American Association For Cancer Research. PMID 29599409 DOI: 10.1158/1078-0432.Ccr-17-1339  0.552
2018 Wagner A, Walsh B, Sonkin D, Dienstmann R, Li XS, Beckmann JS, Mayfield G, Tamborero D, Lopez-Bigas N, Goecks J, Margolin A, Griffith M, Griffith O. 4. Coordinating variant interpretation knowledgebases improves clinical interpretation of genomic variants in cancers Cancer Genetics and Cytogenetics. 37. DOI: 10.1016/J.Cancergen.2018.04.065  0.346
2017 Nikolova O, Moser R, Kemp C, Onen MG, Margolin AA. Modeling Gene-Wise Dependencies Improves the Identification of Drug Response Biomarkers in Cancer Studies. Bioinformatics (Oxford, England). PMID 28082455 DOI: 10.1093/Bioinformatics/Btw836  0.33
2017 Griffith O, Griffith M, Tamborero D, Wagner A, Krysiak K, Fitz CDV, Chakravarty D, Cerami E, Elemento O, Schultz N, Margolin A, Lopez-Bigas N. Global integration of knowledgebases for clinical interpretation of cancer variants F1000research. 6. DOI: 10.7490/F1000Research.1113918.1  0.315
2017 Griffith OL, Griffith M, Tamborero D, Wagner AH, Krysiak K, Fitz CDV, Chakravarty D, Cerami E, Elemento O, Schultz N, Margolin A, Lopez-Bigas N. Abstract 2608: Global integration of knowledgebases for clinical interpretation of cancer variants Cancer Research. 77: 2608-2608. DOI: 10.1158/1538-7445.Am2017-2608  0.315
2016 Gerhard DS, Clemons PA, Shamji AF, Hon C, Wagner BK, Schreiber SL, Krasnitz A, Sordella R, Sander C, Lowe SW, Powers S, Smith K, Aburi M, Iavarone A, Lasorella A, ... ... Margolin AA, et al. Transforming Big Data into cancer-relevant insight: An initial, multi-tier approach to assess reproducibility and relevance. Molecular Cancer Research : McR. PMID 27401613 DOI: 10.1158/1541-7786.Mcr-16-0090  0.542
2015 Ewing AD, Houlahan KE, Hu Y, Ellrott K, Caloian C, Yamaguchi TN, Bare JC, P'ng C, Waggott D, Sabelnykova VY, Kellen MR, Norman TC, Haussler D, Friend SH, ... ... Margolin AA, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nature Methods. PMID 25984700 DOI: 10.1038/Nmeth.3407  0.371
2015 Jang IS, Dienstmann R, Margolin AA, Guinney J. Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 32-43. PMID 25592566  0.617
2014 Boutros PC, Margolin AA, Stuart JM, Califano A, Stolovitzky G. Toward better benchmarking: challenge-based methods assessment in cancer genomics. Genome Biology. 15: 462. PMID 25314947 DOI: 10.1186/S13059-014-0462-7  0.551
2014 Chaibub Neto E, Bare JC, Margolin AA. Simulation studies as designed experiments: the comparison of penalized regression models in the "large p, small n" setting. Plos One. 9: e107957. PMID 25289666 DOI: 10.1371/Journal.Pone.0107957  0.345
2014 Gönen M, Margolin AA. Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinformatics (Oxford, England). 30: i556-63. PMID 25161247 DOI: 10.1093/Bioinformatics/Btu464  0.342
2014 Moser R, Xu C, Kao M, Annis J, Lerma LA, Schaupp CM, Gurley KE, Jang IS, Biktasova A, Yarbrough WG, Margolin AA, Grandori C, Kemp CJ, Méndez E. Functional kinomics identifies candidate therapeutic targets in head and neck cancer. Clinical Cancer Research : An Official Journal of the American Association For Cancer Research. 20: 4274-88. PMID 25125259 DOI: 10.1158/1078-0432.Ccr-13-2858  0.559
2014 Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MD, Niu B, McLellan MD, Uzunangelov V, Zhang J, Kandoth C, Akbani R, Shen H, Omberg L, ... ... Margolin AA, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 158: 929-44. PMID 25109877 DOI: 10.1016/J.Cell.2014.06.049  0.401
2014 Boutros PC, Ewing AD, Ellrott K, Norman TC, Dang KK, Hu Y, Kellen MR, Suver C, Bare JC, Stein LD, Spellman PT, Stolovitzky G, Friend SH, Margolin AA, Stuart JM. Global optimization of somatic variant identification in cancer genomes with a global community challenge. Nature Genetics. 46: 318-9. PMID 24675517 DOI: 10.1038/Ng.2932  0.326
2014 Jang IS, Neto EC, Guinney J, Friend SH, Margolin AA. Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 63-74. PMID 24297534  0.581
2014 Neto EC, Jang IS, Friend SH, Margolin AA. The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 27-38. PMID 24297531  0.534
2014 Ferte C, Chaibub Neto E, Commo F, Nikolova O, Jang iS, Gonen M, Lacroix L, Koubi-Pick V, Angevin E, Besse B, Ducreux M, Hollebecque A, Massard C, Margolin A, Friend SH, et al. Joint cell-line and patient modeling of drug sensitivity reveals novel molecular biomarkers for targeted and conventional chemotherapy. Journal of Clinical Oncology. 32: 2565-2565. DOI: 10.1200/Jco.2014.32.15_Suppl.2565  0.575
2014 Nikolova OH, Gönen M, Dienstmann R, Jang IS, Moser R, Cermelli S, Xu C, Mitchell RM, Mendez E, Grandori C, Kemp C, Friend S, Guinney J, Margolin A. Abstract 5323: Integrated computational cell-line modeling of drug sensitivity and high-throughput siRNA screening reveals novel molecular biomarkers for conventional chemotherapy Cancer Research. 74: 5323-5323. DOI: 10.1158/1538-7445.Am2014-5323  0.621
2014 Margolin AA. To catch a pre-leukemia Science Translational Medicine. 6. DOI: 10.1126/Scitranslmed.3008713  0.301
2014 Margolin AA. Cancer genomics: Moving from unknown unknown to known unknown Science Translational Medicine. 6: 221ec20. DOI: 10.1126/Scitranslmed.3008434  0.363
2013 Jang IS, Margolin A, Califano A. hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. Interface Focus. 3: 20130011. PMID 24511376 DOI: 10.1098/Rsfs.2013.0011  0.691
2013 Omberg L, Ellrott K, Yuan Y, Kandoth C, Wong C, Kellen MR, Friend SH, Stuart J, Liang H, Margolin AA. Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas. Nature Genetics. 45: 1121-6. PMID 24071850 DOI: 10.1038/Ng.2761  0.373
2013 Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, Sauerwine BA, Shimoni Y, Moen Vollan HK, Mecham BH, Rueda OM, Tost J, Curtis C, Alvarez MJ, Kristensen VN, ... ... Margolin AA, et al. Improving breast cancer survival analysis through competition-based multidimensional modeling. Plos Computational Biology. 9: e1003047. PMID 23671412 DOI: 10.1371/Journal.Pcbi.1003047  0.647
2013 Margolin AA, Bilal E, Huang E, Norman TC, Ottestad L, Mecham BH, Sauerwine B, Kellen MR, Mangravite LM, Furia MD, Vollan HK, Rueda OM, Guinney J, Deflaux NA, Hoff B, et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Science Translational Medicine. 5: 181re1. PMID 23596205 DOI: 10.1126/Scitranslmed.3006112  0.375
2013 Shao DD, Tsherniak A, Gopal S, Weir BA, Tamayo P, Stransky N, Schumacher SE, Zack TI, Beroukhim R, Garraway LA, Margolin AA, Root DE, Hahn WC, Mesirov JP. ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Research. 23: 665-78. PMID 23269662 DOI: 10.1101/Gr.143586.112  0.416
2013 Ferté C, Neto EC, Sun C, Noecker C, Commo F, Nikolova O, Jang IS, Gonen M, Besse B, André F, Angevin E, Lacroix L, Mazoyer C, Hollebecque A, Massard C, ... Margolin A, et al. Abstract A130: Joint cell-line (CCLE, Sanger) and patient (TCGA) modeling of drug sensitivity reveals novel molecular biomarkers for targeted therapy and conventional chemotherapy. Molecular Cancer Therapeutics. 12. DOI: 10.1158/1535-7163.Targ-13-A130  0.614
2013 Moser R, Xu C, Kao M, Jang IS, Gurley K, Margolin A, Grandori C, Mendez E, Kemp C. Abstract PR05: Construction of synthetic lethal networks for p53 tumor suppressor pathways identifies candidate therapeutic targets for metastatic, chemotherapy resistant HNSCC Molecular Cancer Therapeutics. 12. DOI: 10.1158/1535-7163.Pms-Pr05  0.573
2013 Margolin AA. Oncogenic driver mutations: Neither tissue-specific nor independent Science Translational Medicine. 5. DOI: 10.1126/Scitranslmed.3008075  0.347
2013 Margolin AA. Single-molecule imaging: It takes two to make a RAF signal right Science Translational Medicine. 5. DOI: 10.1126/Scitranslmed.3007774  0.363
2013 Margolin AA. Co-opting a Tumor Predator to Provide Life Support Science Translational Medicine. 5: 202ec149-202ec149. DOI: 10.1126/Scitranslmed.3007484  0.3
2013 Margolin AA. The enemy of the enemy of my enemy is a therapeutic target Science Translational Medicine. 5. DOI: 10.1126/Scitranslmed.3006748  0.324
2013 Margolin AA. Cancer therapeutics: Best informed by genes or genomes? Science Translational Medicine. 5. DOI: 10.1126/Scitranslmed.3006450  0.412
2013 Margolin AA. Genomics of T-ALL reveals a weapon of an evasive foe Science Translational Medicine. 5. DOI: 10.1126/Scitranslmed.3006150  0.38
2013 Lopez-Bigas N, Cline M, Broom B, Margolin A, Omberg L, Weinstein J, Axton M. Thread 4: Data discovery, transparency and visualization Nature Genetics. DOI: 10.1038/Ng.2789  0.323
2013 Reimand J, Bader G, Margolin A, Gonzalez-Perez A, Tamborero D, Lopez-Bigas N, Weinstein J, Stuart J, Axton M. Thread 2: Network models Nature Genetics. 1-1. DOI: 10.1038/Ng.2787  0.325
2012 Wei G, Margolin AA, Haery L, Brown E, Cucolo L, Julian B, Shehata S, Kung AL, Beroukhim R, Golub TR. Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency. Cancer Cell. 21: 547-62. PMID 22516262 DOI: 10.1016/J.Ccr.2012.02.028  0.304
2012 Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 483: 603-7. PMID 22460905 DOI: 10.1038/Nature11003  0.449
2012 Margolin AA, Jang IS, Friend S. Abstract IA15: Predicting drug sensitivity from cancer cell lines Cancer Research. 72. DOI: 10.1158/1538-7445.Csb12-Ia15  0.627
2012 Stransky N, Kryukov GV, Caponigro G, Barretina J, Venkatesan K, Margolin AA, Wilson CJ, Lehar J, Jones MD, Palescandolo E, Sougnez C, Onofrio RC, MacConaill L, Ardlie K, Golub TR, et al. Abstract 5114: Integrative analysis of the Cancer Cell Line Encyclopedia reveals genetic and transcriptional predictors of compound sensitivity Cancer Research. 72: 5114-5114. DOI: 10.1158/1538-7445.Am2012-5114  0.484
2011 Barretina J, Stransky N, Caponigro G, Kim S, Margolin A, Venkhatesan K, Kryukov G, Berger M, Monahan J, Morais P, Meltzer J, Mahan S, Sonkin D, Raman P, Jones M, et al. Abstract 5455: Integrative analysis of the Cancer Cell Line Encyclopedia reveals genetic and transcriptional predictors of compound sensitivity Cancer Research. 71: 5455-5455. DOI: 10.1158/1538-7445.Am2011-5455  0.459
2011 Wei G, Margolin A, Brown E, Cucolo L, Stransky N, Barretina J, Garraway L, Beroukhim R. Abstract 13: Chemical genomics identifies MCL1 repressors and resistance mechanism Cancer Research. 71: 13-13. DOI: 10.1158/1538-7445.Am2011-13  0.447
2010 Margolin AA, Wang K, Califano A, Nemenman I. Multivariate dependence and genetic networks inference. Iet Systems Biology. 4: 428-40. PMID 21073241 DOI: 10.1049/Iet-Syb.2010.0009  0.704
2010 Basso K, Saito M, Sumazin P, Margolin AA, Wang K, Lim WK, Kitagawa Y, Schneider C, Alvarez MJ, Califano A, Dalla-Favera R. Integrated biochemical and computational approach identifies BCL6 direct target genes controlling multiple pathways in normal germinal center B cells. Blood. 115: 975-84. PMID 19965633 DOI: 10.1182/Blood-2009-06-227017  0.567
2010 Venkatesan K, Stransky N, Margolin A, Reddy A, Raman P, Sonkin D, Jones M, Wilson C, Kim S, Warmuth M, Sellers W, Lehar J, Barretina J, Caponigro G, Garraway L, et al. Prediction of drug response using genomic signatures from the Cancer Cell Line Encyclopedia Clinical Cancer Research. 16. DOI: 10.1158/Diag-10-Pr2  0.452
2010 Du J, Bernasconi P, He F, Julian B, Mani DR, Margolin A, Ross K, Clauser K, Finn SP, Beroukhim R, Burns M, Peng X, Hieronymus H, Maglathlin R, Lewis TA, et al. Abstract 5559: Proteomic identification of activated, essential tyrosine kinases in human cancers Cancer Research. 70: 5559-5559. DOI: 10.1158/1538-7445.Am10-5559  0.406
2010 Stransky N, Venkatesan K, Barretina J, Caponigro G, Margolin A, Morrissey M, Raman P, Getz G, Berger M, Johannessen C, Callahan A, Morais P, Mahan S, Gupta S, Onofrio R, et al. Abstract PR4: Integrative analysis of genomic and pharmacologic data from the Cancer Cell Line Encyclopedia Clinical Cancer Research. 16. DOI: 10.1158/1078-0432.Tcmusa10-Pr4  0.469
2009 Margolin AA, Ong SE, Schenone M, Gould R, Schreiber SL, Carr SA, Golub TR. Empirical Bayes analysis of quantitative proteomics experiments. Plos One. 4: e7454. PMID 19829701 DOI: 10.1371/Journal.Pone.0007454  0.347
2009 Wang K, Saito M, Bisikirska BC, Alvarez MJ, Lim WK, Rajbhandari P, Shen Q, Nemenman I, Basso K, Margolin AA, Klein U, Dalla-Favera R, Califano A. Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nature Biotechnology. 27: 829-39. PMID 19741643 DOI: 10.1038/Nbt.1563  0.701
2009 Ong SE, Schenone M, Margolin AA, Li X, Do K, Doud MK, Mani DR, Kuai L, Wang X, Wood JL, Tolliday NJ, Koehler AN, Marcaurelle LA, Golub TR, Gould RJ, et al. Identifying the proteins to which small-molecule probes and drugs bind in cells. Proceedings of the National Academy of Sciences of the United States of America. 106: 4617-22. PMID 19255428 DOI: 10.1073/Pnas.0900191106  0.34
2009 Margolin AA, Palomero T, Sumazin P, Califano A, Ferrando AA, Stolovitzky G. ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes. Proceedings of the National Academy of Sciences of the United States of America. 106: 244-9. PMID 19118200 DOI: 10.1073/Pnas.0806445106  0.587
2007 Margolin AA, Califano A. Theory and limitations of genetic network inference from microarray data. Annals of the New York Academy of Sciences. 1115: 51-72. PMID 17925348 DOI: 10.1196/Annals.1407.019  0.581
2007 Margolin AA, Palomero T, Ferrando AA, Califano A, Stolovitzky G. ChIP-on-chip significance analysis reveals ubiquitous transcription factor binding Bmc Bioinformatics. 8. DOI: 10.1186/1471-2105-8-S8-S2  0.571
2007 Palomero T, Wei KL, Odom DT, Sulis ML, Real PJ, Margolin A, Barnes KC, O'Neil J, Neuberg D, Weng AP, Aster JC, Sigaux F, Soulier J, Look AT, Young RA, et al. Erratum: NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth (Proceedings of the National Academy of Sciences of the United States of America (2006) 103, 48, (18261-18266) DOI: 10.1073/pnas.0606108103) Proceedings of the National Academy of Sciences of the United States of America. 104. DOI: 10.1073/Pnas.0700840104  0.508
2006 Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A. Reverse engineering cellular networks. Nature Protocols. 1: 662-71. PMID 17406294 DOI: 10.1038/Nprot.2006.106  0.721
2006 Palomero T, Lim WK, Odom DT, Sulis ML, Real PJ, Margolin A, Barnes KC, O'Neil J, Neuberg D, Weng AP, Aster JC, Sigaux F, Soulier J, Look AT, Young RA, et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proceedings of the National Academy of Sciences of the United States of America. 103: 18261-6. PMID 17114293 DOI: 10.1073/Pnas.0606108103  0.549
2006 Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. Bmc Bioinformatics. 7: S7. PMID 16723010 DOI: 10.1186/1471-2105-7-S1-S7  0.709
2006 Palomero T, Odom DT, O'Neil J, Ferrando AA, Margolin A, Neuberg DS, Winter SS, Larson RS, Li W, Liu XS, Young RA, Look AT. Transcriptional regulatory networks downstream of TAL1/SCL in T-cell acute lymphoblastic leukemia. Blood. 108: 986-92. PMID 16621969 DOI: 10.1182/Blood-2005-08-3482  0.345
2006 Wang K, Nemenman I, Banerjee N, Margolin AA, Califano A. Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3909: 348-362.  0.472
2005 Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nature Genetics. 37: 382-90. PMID 15778709 DOI: 10.1038/Ng1532  0.595
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