| 1 |
@Book{knuth:84,
author = {Donald E. Knuth},
title = {The {\TeX} Book},
publisher = {Addison-Wesley},
year = {1984},
edition = {15th}
}
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| 2 |
@InCollection{boulic:91,
author = {R. Boulic and O. Renault},
title = {3D Hierarchies for Animation},
booktitle = {New Trends in Animation and Visualization},
publisher = {John Wiley {\&} Sons ltd.},
year = {1991},
editor = {Nadia Magnenat-Thalmann and Daniel Thalmann}
}
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| 3 |
@InCollection{smith:99,
author = {A. Smith and B. Jones},
title = {On the Complexity of Computing},
booktitle = {Advances in Computer Science},
pages = {555--566},
publisher = {Publishing Press},
year = {1999},
editor = {A. B. Smith-Jones}
}
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| 4 |
@book{10.5555/531075,
author = {Holland, John H.},
title = {Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence},
year = {1992},
isbn = {0262082136},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
abstract = {From the Publisher:Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements. John H. Holland is Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan. He is also Maxwell Professor at the Santa Fe Institute and isDirector of the University of Michigan/Santa Fe Institute Advanced Research Program.}
}
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| 5 |
@article{10.1093/bioadv/vbae103,
author = {Rodella, Chiara and Lazaridi, Symela and Lemmin, Thomas},
title = "{TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms}",
journal = {Bioinformatics Advances},
volume = {4},
number = {1},
pages = {vbae103},
year = {2024},
month = {07},
abstract = "{TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.}",
issn = {2635-0041},
doi = {10.1093/bioadv/vbae103},
url = {https://doi.org/10.1093/bioadv/vbae103},
eprint = {https://academic.oup.com/bioinformaticsadvances/article-pdf/4/1/vbae103/58610069/vbae103.pdf},
}
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| 6 |
@inproceedings{10.5555/3295222.3295349,
author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, \L{}ukasz and Polosukhin, Illia},
title = {Attention is all you need},
year = {2017},
isbn = {9781510860964},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.},
booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
pages = {6000–6010},
numpages = {11},
location = {Long Beach, California, USA},
series = {NIPS'17}
}
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| 7 |
@ARTICLE{9477085,
author={Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and Bhowmik, Debsindhu and Rost, Burkhard},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning},
year={2022},
volume={44},
number={10},
pages={7112-7127},
keywords={Proteins;Training;Amino acids;Task analysis;Databases;Computational modeling;Three-dimensional displays;Computational biology;high performance computing;machine learning;language modeling;deep learning},
doi={10.1109/TPAMI.2021.3095381}}
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| 8 |
@article{rives2021biological,
title={Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences},
author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Liu, Jason and Guo, Demi and Ott, Myle and Zitnick, C Lawrence and Ma, Jerry and others},
journal={Proceedings of the National Academy of Sciences},
volume={118},
number={15},
pages={e2016239118},
year={2021},
publisher={National Acad Sciences},
note={bioRxiv 10.1101/622803},
doi={10.1073/pnas.2016239118},
url={https://www.pnas.org/doi/full/10.1073/pnas.2016239118},
}
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| 9 |
@article{10.1093/nar/gkz321,
author = {Kuriata, Aleksander and Iglesias, Valentin and Pujols, Jordi and Kurcinski, Mateusz and Kmiecik, Sebastian and Ventura, Salvador},
title = {Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility},
journal = {Nucleic Acids Research},
volume = {47},
number = {W1},
pages = {W300-W307},
year = {2019},
month = {05},
abstract = {Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/},
issn = {0305-1048},
doi = {10.1093/nar/gkz321},
url = {https://doi.org/10.1093/nar/gkz321},
eprint = {https://academic.oup.com/nar/article-pdf/47/W1/W300/28879893/gkz321.pdf},
}
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| 10 |
@article{10.1093/nar/gkv359,
author = {Zambrano, Rafael and Jamroz, Michal and Szczasiuk, Agata and Pujols, Jordi and Kmiecik, Sebastian and Ventura, Salvador},
title = {AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures},
journal = {Nucleic Acids Research},
volume = {43},
number = {W1},
pages = {W306-W313},
year = {2015},
month = {04},
abstract = {Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/.},
issn = {0305-1048},
doi = {10.1093/nar/gkv359},
url = {https://doi.org/10.1093/nar/gkv359},
eprint = {https://academic.oup.com/nar/article-pdf/43/W1/W306/17435833/gkv359.pdf},
}
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| 11 |
@article{Bocian2008,
author = {Wojciech Bocian and Jerzy Sitkowski and Elżbieta Bednarek and Anna Tarnowska and Robert Kawęcki and Lech Kozerski},
doi = {10.1007/s10858-007-9206-2},
issn = {0925-2738},
issue = {1},
journal = {Journal of Biomolecular NMR},
month = {1},
pages = {55-64},
title = {Structure of human insulin monomer in water/acetonitrile solution},
volume = {40},
year = {2008}
}
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| 12 |
@article{Sanger1959,
author = {F. Sanger},
doi = {10.1126/science.129.3359.1340},
issn = {0036-8075},
issue = {3359},
journal = {Science},
month = {5},
pages = {1340-1344},
title = {Chemistry of Insulin},
volume = {129},
year = {1959}
}
|
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| 13 |
@article{Polonsky2012,
author = {Kenneth S. Polonsky},
doi = {10.1056/NEJMra1110560},
issn = {0028-4793},
issue = {14},
journal = {New England Journal of Medicine},
month = {10},
pages = {1332-1340},
title = {The Past 200 Years in Diabetes},
volume = {367},
year = {2012}
}
|
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| 14 |
@article{Ogurtsova2017,
author = {K. Ogurtsova and J.D. da Rocha Fernandes and Y. Huang and U. Linnenkamp and L. Guariguata and N.H. Cho and D. Cavan and J.E. Shaw and L.E. Makaroff},
doi = {10.1016/j.diabres.2017.03.024},
issn = {01688227},
journal = {Diabetes Research and Clinical Practice},
month = {6},
pages = {40-50},
title = {IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040},
volume = {128},
year = {2017}
}
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| 15 |
@unpublished{PyMOL,
Annote = {PyMOL The PyMOL Molecular Graphics System, Version 1.8, Schr{\"o}dinger, LLC.},
Author = {{Schr\"odinger, LLC}},
Date-Added = {2010-08-19 17:29:55 -0400},
Date-Modified = {2015-12-22 18:04:08 -0400},
Month = {November},
Title = {The {PyMOL} Molecular Graphics System, Version~1.8},
Year = {2015}}
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