Труды Нобелевских лауреатов 2024 года

Acemoglu, D., Johnson, S., & Robinson, J.A. (2001). The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review, 91(5), pp. 1369-1401. doi: 10.1257/aer.91.5.1369

Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, 4 (PART B), pp. 1043-1171. doi: 10.1016/S0169-7218(11)02410-5

Acemoglu, D., Johnson, S., & Robinson, J.A. (2002). Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution. The Quarterly Journal of Economics, 117(4), pp. 1231-1294. doi: 10.1162/003355302320935025

Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous (2012). The Environment and Directed Technical Change. American Economic Review, 102 (1), pp. 131–166. doi: 10.1257/aer.102.1.131

Acemoglu, Daron. (2002). Technical Change, Inequality, and the Labor Market. Journal of Economic Literature, 40 (1), pp. 7–72. doi: 10.1257/0022051026976

Acemoglu, D., Johnson, S., & Robinson, J.A. (2001). The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review, 91(5), pp. 1369-1401. doi: 10.1257/aer.91.5.1369

Acemoglu, D., Johnson, S., & Robinson, J.A. (2002). Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution. The Quarterly Journal of Economics, 117(4), pp. 1231-1294. doi: 10.1162/003355302320935025

Simon Johnson, Rafael La Porta, Florencio Lopez-de-Silanes, Andrei Shleifer (2000). Tunneling. The American Economic Review, Vol. 90, No. 2, Papers and Proceedings of the One Hundred Twelfth Annual Meeting of the American Economic Association, pp. 22-27. doi: 10.1257/aer.90.2.22

Simon Johnson, Peter Boone, Alasdair Breach, Eric Friedman (2000). Corporate governance in the Asian financial crisis. Journal of Financial Economics, 58(1-2), pp. 141-186. doi: 10.1016/s0304-405x(00)00069-6

Daron Acemoglu, Simon Johnson, James A. Robinson (2005). Chapter 6 Institutions as a Fundamental Cause of Long-Run Growth. Handbook of Economic Growth, Vol. 1, pp. 385-472. doi: 10.1016/s1574-0684(05)01006-3
 

Acemoglu, D., Johnson, S., & Robinson, J.A. (2001). The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review, 91(5), pp. 1369-1401. doi: 10.1257/aer.91.5.1369

Acemoglu, D., Johnson, S., & Robinson, J.A. (2002). Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution. The Quarterly Journal of Economics, 117(4), pp. 1231-1294. doi: 10.1162/003355302320935025

Daron Acemoglu, Simon Johnson, James A. Robinson (2005). Chapter 6 Institutions as a Fundamental Cause of Long-Run Growth. Handbook of Economic Growth, Vol. 1, pp. 385-472. doi: 10.1016/s1574-0684(05)01006-3

Daron Acemoglu, Simon Johnson, James Robinson (2005). The Rise of Europe: Atlantic Trade, Institutional Change, and Economic Growth. American Economic Review, 95(3), pp. 546-579. doi: 10.1257/0002828054201305

Daron Acemoglu, Simon Johnson, James Robinson, Yunyong Thaicharoen (2003). Institutional causes, macroeconomic symptoms: volatility, crises and growth. Journal of Monetary Economics, 50(1), pp. 49-123. doi: 10.1016/s0304-3932(02)00208-8

J J Hopfield  (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), pp. 2554-2558. doi: 10.1073/pnas.79.8.2554

J J Hopfield  (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America, 81(10), pp. 3088-3092. doi: 10.1073/pnas.81.10.3088

J. J. Hopfield, D. W. Tank (1985). “Neural” computation of decisions in optimization problems. Biological Cybernetics, 52(3), pp; 141-152. doi: 10.1007/bf00339943

Leland H. Hartwell, John J. Hopfield, Stanislas Leibler, Andrew W. Murray (1999). From molecular to modular cell biology. Nature, 402 (Suppl 6761), pp: c47-c52. doi: 10.1038/35011540

D. Tank, J. Hopfield (1986). Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. Circuits and Systems, 33(5), pp. 533-541. doi: 10.1109/tcs.1986.1085953
 

Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015). Deep learning. Nature, 521(7553), pp. 436-444. doi: 10.1038/nature14539 

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), pp. 84-90. doi: 10.1145/3065386 

David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986). Learning representations by back-propagating errors. Nature, 323(6088), pp; 533-536. doi: 10.1038/323533a0

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan SalakhutdinovAuthors (2014).  Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, Volume 15, Issue 1, pp. 1929 - 1958. doi: 10.5555/2627435.2670313

D. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986). Learning representations by back-propagating errors. Nature volume 323, pp. 533–536. doi: 10.1038/323533a0
 

Victor Ambros (2004). The functions of animal microRNAs. Nature, 431(7006), pp. 350-355. doi: 10.1038/nature02871

C.C. Mello, J.M. Kramer, D. Stinchcomb, V. Ambros (1991). Efficient gene transfer in C.elegans: extrachromosomal maintenance and integration of transforming sequences. The EMBO Journal, 10(12), pp. 3959-3970. doi: 10.1002/j.1460-2075.1991.tb04966.x

Rosalind C. Lee, Victor Ambros (2001). An Extensive Class of Small RNAs in Caenorhabditis elegans. Science, 294(5543), pp. 862-864. doi: 10.1126/science.1065329

Victor Ambros, Bonnie Bartel, David P Bartel, Christopher B Burge, James C Carrington, Xuemei Chen, Gideon Dreyfuss, Sean R Eddy, Sam Griffiths-Jones, Mhairi Marshall, Marjori Matzke, Gary Ruvkun, Thomas Tuschl (2003). A uniform system for microRNA annotation. RNA, 9(3), pp. 277-279. doi: 10.1261/rna.2183803

James C. Carrington, Victor Ambros (2003). Role of MicroRNAs in Plant and Animal Development. Science, 301(5631), pp. 336-338. doi: 10.1126/science.1085242

Brenda J. Reinhart, Frank J. Slack, Michael Basson, Amy E. Pasquinelli, Jill C. Bettinger, Ann E. Rougvie, H. Robert Horvitz, Gary Ruvkun (2000). The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 403(6772), pp. 901-906. doi: 10.1038/35002607

Amy E. Pasquinelli, Brenda J. Reinhart, Frank Slack, Mark Q. Martindale, Mitzi I. Kuroda, Betsy Maller, David C. Hayward, Eldon E. Ball, Bernard Degnan, Peter Müller, Jürg Spring, Ashok Srinivasan, Mark Fishman, John Finnerty, Joseph Corbo, Michael Levine, Patrick Leahy, Eric Davidson, Gary Ruvkun (2000). Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature, 408(6808), pp. 86-89. doi: 10.1038/35040556

Koutarou D. Kimura, Heidi A. Tissenbaum, Yanxia Liu, Gary Ruvkun (1997). daf-2, an Insulin Receptor-Like Gene That Regulates Longevity and Diapause in Caenorhabditis elegans. Science, 277(5328), pp. 942-946. doi: 10.1126/science.277.5328.942

Scott Ogg, Suzanne Paradis, Shoshanna Gottlieb, Garth I. Patterson, Linda Lee, Heidi A. Tissenbaum, Gary Ruvkun (1997). The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature, 389(6654), pp. 994-999. doi: 10.1038/40194

Victor Ambros, Bonnie Bartel, David P Bartel, Christopher B Burge, James C Carrington, Xuemei Chen, Gideon Dreyfuss, Sean R Eddy, Sam Griffiths-Jones, Mhairi Marshall, Marjori Matzke, Gary Ruvkun, Thomas Tuschl (2003). A uniform system for microRNA annotation. RNA, 9(3), pp. 277-279. doi: 10.1261/rna.2183803

David E Kim, Dylan Chivian, David Baker (2004). Protein structure prediction and analysis using the Robetta server. Nucleic Acids Research, 32(suppl_2), pp. w526-w531. doi: 10.1093/nar/gkh468

Brian Kuhlman, Gautam Dantas, Gregory C. Ireton, Gabriele Varani, Barry L. Stoddard, David Baker (2003). Design of a Novel Globular Protein Fold with Atomic-Level Accuracy. Science, 302(5649), pp. 1364-1368. doi: 10.1126/science.1089427

Eranthie Weerapana, Chu Wang, Gabriel M. Simon, Florian Richter, Sagar Khare, Myles B. D. Dillon, Daniel A. Bachovchin, Kerri Mowen, David Baker, Benjamin F. Cravatt (2010). Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature, 468(7325), pp. 790-795. doi: 10.1038/nature09472

David Baker, Andrej Sali (2001). Protein Structure Prediction and Structural Genomics. Science, 294(5540), pp. 93-96. doi: 10.1126/science.1065659

Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, David Baker (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences of the United States of America, 117(3), pp. 1496-1503. doi: 10.1073/pnas.1914677117
 

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), pp. 529-533. doi: 10.1038/nature14236

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), pp. 583-589. doi: 10.1038/s41586-021-03819-2

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), pp. 484-489. doi: 10.1038/nature16961

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), pp. 354-359. doi: 10.1038/nature24270

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences of the United States of America, 114(13), pp. 3521-3526. doi: 10.1073/pnas.1611835114

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), pp. 583-589. doi: 10.1038/s41586-021-03819-2

Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), pp. 706-710. doi: 10.1038/s41586-019-1923-7

Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar (2021). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, 50(D1), pp. d439-d444. doi: 10.1093/nar/gkab1061

Richard Evans, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian Bodenstein, Michal Zielinski, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, Demis Hassabis (2021). Protein complex prediction with AlphaFold-Multimer. bioRxiv - October 2021. doi: 10.1101/2021.10.04.463034

Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A. A. Kohl, Anna Potapenko, Andrew J. Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W. Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873), pp. 590-596. doi: 10.1038/s41586-021-03828-1