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pro vyhledávání: '"De Cock M"'
Publikováno v:
Rev. Math. Phys., Vol. 15 Num. 8 (October 2003) 847-875
Two non-commutative dynamical entropies are studied in connection with the classical limit. For systems with a strongly chaotic classical limit, the Kolmogorov-Sinai invariant is recovered on time scales that are logarithmic in the quantisation param
Externí odkaz:
http://arxiv.org/abs/quant-ph/0308069
Publikováno v:
Rept.Math.Phys. 44 (1999) 413-434
The long time asymptotics of multi-time correlation functions of relaxing quantum mechanical systems can be conveniently studied by means of free-products of suitable C*-algebras and of states on these free products given by multiple temporal average
Externí odkaz:
http://arxiv.org/abs/math-ph/9901010
Publikováno v:
Rev.Math.Phys. 12 (2000) 921-944
In this paper, we consider the long time asymptotics of multi-time correlation functions for quantum dynamical systems that are sufficiently random to relax to a ``reference state''. In particular, the evolution of such systems must have a continuous
Externí odkaz:
http://arxiv.org/abs/quant-ph/9811064
Autor:
De Cock Miriam
Publikováno v:
Open Theology, Vol 10, Iss 1, Pp 229-48 (2024)
In this article, I wish to build on the little work previously done on the theme of Origen and women by focusing on Origen’s exegetical treatment of the speech uttered by a selection of female scriptural characters. I focus on their speech as a way
Externí odkaz:
https://doaj.org/article/e9e78d6e129749b0a4832cd7394fe0ba
Akademický článek
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Publikováno v:
In Information Sciences 2010 180(17):3192-3209
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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
instname
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
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
r-FISABIO. Repositorio Institucional de Producción Científica
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Nature Communications, 10(1):2674. Nature Publishing Group
Recercat. Dipósit de la Recerca de Catalunya
Nature Communications
Nature Communications, 10, 2674
r-IGTP. Repositorio Institucional de Producción Científica del Instituto de Investigación Germans Trias i Pujol
instname
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
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
r-FISABIO. Repositorio Institucional de Producción Científica
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
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
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
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