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Akademický článek
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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
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
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Autor:
Seidl, Philipp, Renz, Philipp, Dyubankova, Natalia, Neves, Paulo, Verhoeven, Jonas, Segler, Marwin, Wegner, J��rg K., Hochreiter, Sepp, Klambauer, G��nter
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2a97ca265052784c38ee9aa9a060866
http://arxiv.org/abs/2104.03279
http://arxiv.org/abs/2104.03279
Autor:
Ramsauer, Hubert, Sch��fl, Bernhard, Lehner, Johannes, Seidl, Philipp, Widrich, Michael, Adler, Thomas, Gruber, Lukas, Holzleitner, Markus, Pavlovi��, Milena, Sandve, Geir Kjetil, Greiff, Victor, Kreil, David, Kopp, Michael, Klambauer, G��nter, Brandstetter, Johannes, Hochreiter, Sepp
We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10c53b0bc55eedc13ef6a44db3ab5f2c
http://arxiv.org/abs/2008.02217
http://arxiv.org/abs/2008.02217
Autor:
Widrich, Michael, Sch��fl, Bernhard, Ramsauer, Hubert, Pavlovi��, Milena, Gruber, Lukas, Holzleitner, Markus, Brandstetter, Johannes, Sandve, Geir Kjetil, Greiff, Victor, Hochreiter, Sepp, Klambauer, G��nter
Publikováno v:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Bernhard Schäfl
Bernhard Schäfl
A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanis
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Kratzert, Frederik, Klotz, Daniel, Shalev, Guy, Klambauer, G��nter, Hochreiter, Sepp, Nearing, Grey
Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins togethe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4751f8b5829b8b81012ff56cb1666d1a
http://arxiv.org/abs/1907.08456
http://arxiv.org/abs/1907.08456
Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e7c065629ea1cfccfd302de8029abf4
http://arxiv.org/abs/1903.07903
http://arxiv.org/abs/1903.07903