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9 results on '"Bridgland, Alex"'

1. Accurate structure prediction of biomolecular interactions with AlphaFold 3

Author

Abramson, Josh, Adler, Jonas, Dunger, Jack, Evans, Richard, Green, Tim, Pritzel, Alexander, Ronneberger, Olaf, Willmore, Lindsay, Ballard, Andrew J., Bambrick, Joshua, Bodenstein, Sebastian W., Evans, David A., Hung, Chia-Chun, O’Neill, Michael, Reiman, David, Tunyasuvunakool, Kathryn, Wu, Zachary, Žemgulytė, Akvilė, Arvaniti, Eirini, Beattie, Charles, Bertolli, Ottavia, Bridgland, Alex, Cherepanov, Alexey, Congreve, Miles, Cowen-Rivers, Alexander I., Cowie, Andrew, Figurnov, Michael, Fuchs, Fabian B., Gladman, Hannah, Jain, Rishub, Khan, Yousuf A., Low, Caroline M. R., Perlin, Kuba, Potapenko, Anna, Savy, Pascal, Singh, Sukhdeep, Stecula, Adrian, Thillaisundaram, Ashok, Tong, Catherine, Yakneen, Sergei, Zhong, Ellen D., Zielinski, Michal, Žídek, Augustin, Bapst, Victor, Kohli, Pushmeet, Jaderberg, Max, Hassabis, Demis, and Jumper, John M.

Published
2024
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2. Game Plan: What AI can do for Football, and What Football can do for AI

Author

Tuyls, Karl, Omidshafiei, Shayegan, Muller, Paul, Wang, Zhe, Connor, Jerome, Hennes, Daniel, Graham, Ian, Spearman, William, Waskett, Tim, Steele, Dafydd, Luc, Pauline, Recasens, Adria, Galashov, Alexandre, Thornton, Gregory, Elie, Romuald, Sprechmann, Pablo, Moreno, Pol, Cao, Kris, Garnelo, Marta, Dutta, Praneet, Valko, Michal, Heess, Nicolas, Bridgland, Alex, Perolat, Julien, De Vylder, Bart, Eslami, Ali, Rowland, Mark, Jaegle, Andrew, Munos, Remi, Back, Trevor, Ahamed, Razia, Bouton, Simon, Beauguerlange, Nathalie, Broshear, Jackson, Graepel, Thore, and Hassabis, Demis

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory, Computer Science - Multiagent Systems
Abstract

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

Published
2020

3. Highly accurate protein structure prediction for the human proteome

Author

Tunyasuvunakool, Kathryn, Adler, Jonas, Wu, Zachary, Green, Tim, Zielinski, Michal, Zídek, Augustin, and Bridgland, Alex

Subjects
Protein structure prediction -- Methods, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure.sup.1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold.sup.2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome--the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective., Author(s): Kathryn Tunyasuvunakool [sup.1] , Jonas Adler [sup.1] , Zachary Wu [sup.1] , Tim Green [sup.1] , Michal Zielinski [sup.1] , Augustin Zídek [sup.1] , Alex Bridgland [sup.1] , Andrew [...]

Published
2021
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4. Highly accurate protein structure prediction with AlphaFold

Author

Jumper, John, Evans, Richard, Pritzel, Alexander, Green, Tim, Figurnov, Michael, Ronneberger, Olaf, Tunyasuvunakool, Kathryn, Bates, Russ, Žídek, Augustin, Potapenko, Anna, Bridgland, Alex, Meyer, Clemens, Kohl, Simon A. A., Ballard, Andrew J., Cowie, Andrew, Romera-Paredes, Bernardino, Nikolov, Stanislav, Jain, Rishub, Adler, Jonas, Back, Trevor, Petersen, Stig, Reiman, David, Clancy, Ellen, Zielinski, Michal, Steinegger, Martin, Pacholska, Michalina, Berghammer, Tamas, Bodenstein, Sebastian, Silver, David, Vinyals, Oriol, Senior, Andrew W., Kavukcuoglu, Koray, Kohli, Pushmeet, and Hassabis, Demis

Published
2021
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5. Improved protein structure prediction using potentials from deep learning

Author

Senior, Andrew W., Evans, Richard, Jumper, John, Kirkpatrick, James, Sifre, Laurent, Green, Tim, Qin, Chongli, Žídek, Augustin, Nelson, Alexander W. R., Bridgland, Alex, Penedones, Hugo, Petersen, Stig, Simonyan, Karen, Crossan, Steve, Kohli, Pushmeet, Jones, David T., Silver, David, Kavukcuoglu, Koray, and Hassabis, Demis

Published
2020
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6. Applying and improving AlphaFold at CASP14

Author

Jumper, John, primary, Evans, Richard, additional, Pritzel, Alexander, additional, Green, Tim, additional, Figurnov, Michael, additional, Ronneberger, Olaf, additional, Tunyasuvunakool, Kathryn, additional, Bates, Russ, additional, Žídek, Augustin, additional, Potapenko, Anna, additional, Bridgland, Alex, additional, Meyer, Clemens, additional, Kohl, Simon A. A., additional, Ballard, Andrew J., additional, Cowie, Andrew, additional, Romera‐Paredes, Bernardino, additional, Nikolov, Stanislav, additional, Jain, Rishub, additional, Adler, Jonas, additional, Back, Trevor, additional, Petersen, Stig, additional, Reiman, David, additional, Clancy, Ellen, additional, Zielinski, Michal, additional, Steinegger, Martin, additional, Pacholska, Michalina, additional, Berghammer, Tamas, additional, Silver, David, additional, Vinyals, Oriol, additional, Senior, Andrew W., additional, Kavukcuoglu, Koray, additional, Kohli, Pushmeet, additional, and Hassabis, Demis, additional

Published
2021
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7. Protein complex prediction with AlphaFold-Multimer

Author

Evans, Richard, primary, O’Neill, Michael, additional, Pritzel, Alexander, additional, Antropova, Natasha, additional, Senior, Andrew, additional, Green, Tim, additional, Žídek, Augustin, additional, Bates, Russ, additional, Blackwell, Sam, additional, Yim, Jason, additional, Ronneberger, Olaf, additional, Bodenstein, Sebastian, additional, Zielinski, Michal, additional, Bridgland, Alex, additional, Potapenko, Anna, additional, Cowie, Andrew, additional, Tunyasuvunakool, Kathryn, additional, Jain, Rishub, additional, Clancy, Ellen, additional, Kohli, Pushmeet, additional, Jumper, John, additional, and Hassabis, Demis, additional

Published
2021
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8. Game Plan: What AI can do for Football, and What Football can do for AI

Author

Tuyls, Karl, primary, Omidshafiei, Shayegan, additional, Muller, Paul, additional, Wang, Zhe, additional, Connor, Jerome, additional, Hennes, Daniel, additional, Graham, Ian, additional, Spearman, William, additional, Waskett, Tim, additional, Steel, Dafydd, additional, Luc, Pauline, additional, Recasens, Adria, additional, Galashov, Alexandre, additional, Thornton, Gregory, additional, Elie, Romuald, additional, Sprechmann, Pablo, additional, Moreno, Pol, additional, Cao, Kris, additional, Garnelo, Marta, additional, Dutta, Praneet, additional, Valko, Michal, additional, Heess, Nicolas, additional, Bridgland, Alex, additional, Pérolat, Julien, additional, De Vylder, Bart, additional, Eslami, S. M. Ali, additional, Rowland, Mark, additional, Jaegle, Andrew, additional, Munos, Remi, additional, Back, Trevor, additional, Ahamed, Razia, additional, Bouton, Simon, additional, Beauguerlange, Nathalie, additional, Broshear, Jackson, additional, Graepel, Thore, additional, and Hassabis, Demis, additional

Published
2021
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9. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

Author

Senior, Andrew W., primary, Evans, Richard, additional, Jumper, John, additional, Kirkpatrick, James, additional, Sifre, Laurent, additional, Green, Tim, additional, Qin, Chongli, additional, Žídek, Augustin, additional, Nelson, Alexander W. R., additional, Bridgland, Alex, additional, Penedones, Hugo, additional, Petersen, Stig, additional, Simonyan, Karen, additional, Crossan, Steve, additional, Kohli, Pushmeet, additional, Jones, David T., additional, Silver, David, additional, Kavukcuoglu, Koray, additional, and Hassabis, Demis, additional

Published
2019
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