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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Autor: Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio

Publikováno v: Nature Communications, Vol 10, Iss 1, Pp 1-17 (2019)
Nature Communications
Nature Communications, 10, 2674
r-IGTP. Repositorio Institucional de Producción Científica del Instituto de Investigación Germans Trias i Pujol
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Nature Communications, 10, pp. 1-17
Nature Communications 10, 2674 (2019). doi:10.1038/s41467-019-09799-2
Dipòsit Digital de la UB
Universidad de Barcelona
Nature communications, 10 (1
AstraZeneca-Sanger Drug Combination DREAM Consortium 2019, ' Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen ', Nature Communications, vol. 10, no. 1, 2674 . https://doi.org/10.1038/s41467-019-09799-2
Nature Communications, 10:2674. Nature Publishing Group
NATURE COMMUNICATIONS
Nature Communications, 10, 1-17
Nature communications, vol 10, iss 1
Menden, M P, Wang, D, Mason, M J, Szalai, B, Bulusu, K C, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang, I S, Ghazoui, Z, Ahsen, M E, Vogel, R, Neto, E C, Norman, T, Tang, E K Y, Garnett, M J, Veroli, G Y D, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J R & Saez-Rodriguez, J 2019, ' Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen ', Nature Communications, vol. 10, no. 1, 2674 . https://doi.org/10.1038/s41467-019-09799-2
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Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
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Nature Communications, 10(1):2674. Nature Publishing Group
Recercat. Dipósit de la Recerca de Catalunya

PubMed: 31209238
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1827ac4799243e82b5025526f2e5a77a
http://link.springer.com/article/10.1038/s41467-019-09799-2

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1.17 - Multiset Data Analysis: ANOVA Simultaneous Component Analysis and Related Methods

Autor: Hoefsloot, H. C. J., Vis, D. J., Westerhuis, J. A., Smilde, A. K., Jansen, J. J.

Publikováno v: Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set, 1, 465-478

Data sets resulting from metabolomics, proteomics, or metabolic profiling experiments are usually complex. This type of data contains underlying factors, such as time, doses, or combinations thereof. Classical biostatistics methods do not take into a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::556a34fb1dc1a2ff0ecf3da1ba6d03b8
https://pure.amc.nl/en/publications/117--multiset-data-analysis(5e9eeef1-33e3-4329-a826-cf68804a2fe5).html

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