AlphaFold (Czech Wikipedia)

Analysis of information sources in references of the Wikipedia article "AlphaFold" in Czech language version.

refsWebsite

Global rank Czech rank

22doi.org

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14worldcat.org

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9nih.gov

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7nature.com

234^th place

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3sciencedirect.com

149^th place

80^th place

2deepmind.com

low place

2wiley.com

222^nd place

83^rd place

2sciencemag.org

857^th place

315^th place

1technologyreview.com

1,943^rd place

2,271^st place

1iza.org

low place

1cosmeticsandtoiletries.com

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1web.archive.org

1^st place

1mdpi.com

2,912^th place

482^nd place

1chemistryworld.com

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3,664^th place

1medium.com

551^st place

618^th place

1occamstypewriter.org

low place

1skeptic.org.uk

low place

1github.com

383^rd place

1,113^th place

1ebi.ac.uk

5,849^th place

low place

chemistryworld.com

BALL2020-12-09T14:40:00+00:00, Philip. Behind the screens of AlphaFold. Chemistry World [online]. [cit. 2021-04-14]. Dostupné online. (anglicky)

cosmeticsandtoiletries.com

PARIS, Karl Lintner, Ph D. , KAL’idées Beauty Ideas. Could Protein Folding Reshape Anti-aging Cosmetics?. Cosmetics & Toiletries [online]. [cit. 2021-04-14]. Dostupné online.

deepmind.com

AlphaFold. Deepmind [online]. [cit. 2021-04-14]. Dostupné online.
AlphaFold: Using AI for scientific discovery. Deepmind [online]. [cit. 2021-04-14]. Dostupné online.

doi.org

dx.doi.org

ALQURAISHI, Mohammed. AlphaFold at CASP13. Bioinformatics. 2019-05-22, roč. 35, čís. 22, s. 4862–4865. Dostupné online [cit. 2021-04-14]. ISSN 1367-4803. DOI 10.1093/bioinformatics/btz422. PMID 31116374.
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Improved protein structure prediction using potentials from deep learning. Nature. 2020-01, roč. 577, čís. 7792, s. 706–710. Dostupné online [cit. 2021-04-14]. ISSN 1476-4687. DOI 10.1038/s41586-019-1923-7. (anglicky)
CALLAWAY, Ewen. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, roč. 588, čís. 7837, s. 203–204. Dostupné online [cit. 2021-04-14]. DOI 10.1038/d41586-020-03348-4. (anglicky)
FLOWER, Thomas G.; HURLEY, James H. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. Protein Science. 2021, roč. 30, čís. 4, s. 728–734. Dostupné online [cit. 2021-04-14]. ISSN 1469-896X. DOI 10.1002/pro.4050. PMID 33625752. (anglicky)
HEO, Lim; FEIG, Michael. Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement. bioRxiv. 2020-03-28. PMID 32511334 PMCID: PMC7239069. Dostupné online [cit. 2021-04-14]. DOI 10.1101/2020.03.25.008904. PMID 32511334.
SERVICE, Robert F. ‘The game has changed.’ AI triumphs at protein folding. Science. 2020-12-04, roč. 370, čís. 6521, s. 1144–1145. PMID 33273077. Dostupné online [cit. 2021-04-14]. ISSN 0036-8075. DOI 10.1126/science.370.6521.1144. PMID 33273077. (anglicky)
CALLAWAY, Ewen. ‘The entire protein universe’: AI predicts shape of nearly every known protein. Nature. 2022-07-28, roč. 608, čís. 7921, s. 15–16. Dostupné online [cit. 2022-09-02]. DOI 10.1038/d41586-022-02083-2. (anglicky)
NGO, J. Thomas; MARKS, Joe; KARPLUS, Martin. Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox. Příprava vydání Kenneth M. Merz, Scott M. Le Grand. Boston, MA: Birkhäuser Dostupné online. ISBN 978-1-4684-6831-1. DOI 10.1007/978-1-4684-6831-1_14. S. 433–506. (anglicky) DOI: 10.1007/978-1-4684-6831-1_14.
DILL, Ken A.; OZKAN, S. Banu; SHELL, M. Scott. The Protein Folding Problem. Annual review of biophysics. 2008-06-09, roč. 37, s. 289–316. PMID 18573083 PMCID: PMC2443096. Dostupné online [cit. 2021-04-14]. ISSN 1936-122X. DOI 10.1146/annurev.biophys.37.092707.153558. PMID 18573083.
Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-01, roč. 18, s. 1301–1310. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. (anglicky)
TORRISI, Mirko; POLLASTRI, Gianluca; LE, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-22, roč. 18, s. 1301–1310. PMID 32612753 PMCID: PMC7305407. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. PMID 32612753.
ALQURAISHI, Mohammed. A watershed moment for protein structure prediction. Nature. 2020-01, roč. 577, čís. 7792, s. 627–628. Dostupné online [cit. 2021-04-14]. DOI 10.1038/d41586-019-03951-0. (anglicky)
ZWANZIG, R.; SZABO, A.; BAGCHI, B. Levinthal's paradox. Proceedings of the National Academy of Sciences. 1992-01-01, roč. 89, čís. 1, s. 20–22. PMID 1729690. Dostupné v archivu pořízeném dne 2021-04-14. ISSN 0027-8424. DOI 10.1073/pnas.89.1.20. PMID 1729690. (anglicky) Archivováno 14. 4. 2021 na Wayback Machine.
The Levinthal paradox: yesterday and today. Folding and Design. 1997-06-01, roč. 2, s. S69–S75. Dostupné online [cit. 2021-04-14]. ISSN 1359-0278. DOI 10.1016/S1359-0278(97)00067-9. (anglicky)
Generative deep learning for macromolecular structure and dynamics. Current Opinion in Structural Biology. 2021-04-01, roč. 67, s. 170–177. Dostupné online [cit. 2021-04-14]. ISSN 0959-440X. DOI 10.1016/j.sbi.2020.11.012. (anglicky)
SINGH, Arunima. Deep learning 3D structures. Nature Methods. 2020-03, roč. 17, čís. 3, s. 249–249. Dostupné online [cit. 2021-04-14]. ISSN 1548-7105. DOI 10.1038/s41592-020-0779-y. (anglicky)
GRAVES, Jordan; BYERLY, Jacob; PRIEGO, Eduardo. A Review of Deep Learning Methods for Antibodies. Antibodies. 2020/6, roč. 9, čís. 2, s. 12. Dostupné online [cit. 2021-04-14]. DOI 10.3390/antib9020012. (anglicky)
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins: Structure, Function, and Bioinformatics. 2019, roč. 87, čís. 12, s. 1141–1148. Dostupné online [cit. 2021-04-14]. ISSN 1097-0134. DOI 10.1002/prot.25834. PMID 31602685. (anglicky)
JUMPER, John; EVANS, Richard; PRITZEL, Alexander. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-08, roč. 596, čís. 7873, s. 583–589. Dostupné online [cit. 2022-09-02]. ISSN 1476-4687. DOI 10.1038/s41586-021-03819-2. PMID 34265844. (anglicky)
HEKKELMAN, Maarten L.; DE VRIES, Ida; JOOSTEN, Robbie P. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nature Methods. 2022-11-24, s. 1–9. Dostupné online [cit. 2022-12-10]. ISSN 1548-7105. DOI 10.1038/s41592-022-01685-y. (anglicky)

doi.org

ALQURAISHI, Mohammed. AlphaFold at CASP13. Bioinformatics. 2019-05-22, roč. 35, čís. 22, s. 4862–4865. Dostupné online [cit. 2021-04-14]. ISSN 1367-4803. DOI 10.1093/bioinformatics/btz422. PMID 31116374.
NGO, J. Thomas; MARKS, Joe; KARPLUS, Martin. Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox. Příprava vydání Kenneth M. Merz, Scott M. Le Grand. Boston, MA: Birkhäuser Dostupné online. ISBN 978-1-4684-6831-1. DOI 10.1007/978-1-4684-6831-1_14. S. 433–506. (anglicky) DOI: 10.1007/978-1-4684-6831-1_14.

ebi.ac.uk

alphafold.ebi.ac.uk

AlphaFold Protein Structure Database. alphafold.ebi.ac.uk [online]. [cit. 2022-09-02]. Dostupné online.

github.com

AlphaFold. github.com [online]. 2022-09-01 [cit. 2022-09-02]. Original-date: 2021-06-17T14:06:06Z. Dostupné online.

iza.org

Artificial Intelligence against COVID-19: An Early Review. www.iza.org [online]. [cit. 2021-04-14]. Dostupné online. (anglicky)

mdpi.com

GRAVES, Jordan; BYERLY, Jacob; PRIEGO, Eduardo. A Review of Deep Learning Methods for Antibodies. Antibodies. 2020/6, roč. 9, čís. 2, s. 12. Dostupné online [cit. 2021-04-14]. DOI 10.3390/antib9020012. (anglicky)

medium.com

timjph.medium.com

HUBBARD, Tim. The secret of life, part 2: the solution of the protein folding problem.. Medium [online]. 2020-12-07 [cit. 2021-04-14]. Dostupné online. (anglicky)

nature.com

SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Improved protein structure prediction using potentials from deep learning. Nature. 2020-01, roč. 577, čís. 7792, s. 706–710. Dostupné online [cit. 2021-04-14]. ISSN 1476-4687. DOI 10.1038/s41586-019-1923-7. (anglicky)
CALLAWAY, Ewen. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, roč. 588, čís. 7837, s. 203–204. Dostupné online [cit. 2021-04-14]. DOI 10.1038/d41586-020-03348-4. (anglicky)
CALLAWAY, Ewen. ‘The entire protein universe’: AI predicts shape of nearly every known protein. Nature. 2022-07-28, roč. 608, čís. 7921, s. 15–16. Dostupné online [cit. 2022-09-02]. DOI 10.1038/d41586-022-02083-2. (anglicky)
ALQURAISHI, Mohammed. A watershed moment for protein structure prediction. Nature. 2020-01, roč. 577, čís. 7792, s. 627–628. Dostupné online [cit. 2021-04-14]. DOI 10.1038/d41586-019-03951-0. (anglicky)
SINGH, Arunima. Deep learning 3D structures. Nature Methods. 2020-03, roč. 17, čís. 3, s. 249–249. Dostupné online [cit. 2021-04-14]. ISSN 1548-7105. DOI 10.1038/s41592-020-0779-y. (anglicky)
JUMPER, John; EVANS, Richard; PRITZEL, Alexander. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-08, roč. 596, čís. 7873, s. 583–589. Dostupné online [cit. 2022-09-02]. ISSN 1476-4687. DOI 10.1038/s41586-021-03819-2. PMID 34265844. (anglicky)
HEKKELMAN, Maarten L.; DE VRIES, Ida; JOOSTEN, Robbie P. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nature Methods. 2022-11-24, s. 1–9. Dostupné online [cit. 2022-12-10]. ISSN 1548-7105. DOI 10.1038/s41592-022-01685-y. (anglicky)

nih.gov

ncbi.nlm.nih.gov

ALQURAISHI, Mohammed. AlphaFold at CASP13. Bioinformatics. 2019-05-22, roč. 35, čís. 22, s. 4862–4865. Dostupné online [cit. 2021-04-14]. ISSN 1367-4803. DOI 10.1093/bioinformatics/btz422. PMID 31116374.
FLOWER, Thomas G.; HURLEY, James H. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. Protein Science. 2021, roč. 30, čís. 4, s. 728–734. Dostupné online [cit. 2021-04-14]. ISSN 1469-896X. DOI 10.1002/pro.4050. PMID 33625752. (anglicky)
HEO, Lim; FEIG, Michael. Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement. bioRxiv. 2020-03-28. PMID 32511334 PMCID: PMC7239069. Dostupné online [cit. 2021-04-14]. DOI 10.1101/2020.03.25.008904. PMID 32511334.
SERVICE, Robert F. ‘The game has changed.’ AI triumphs at protein folding. Science. 2020-12-04, roč. 370, čís. 6521, s. 1144–1145. PMID 33273077. Dostupné online [cit. 2021-04-14]. ISSN 0036-8075. DOI 10.1126/science.370.6521.1144. PMID 33273077. (anglicky)
DILL, Ken A.; OZKAN, S. Banu; SHELL, M. Scott. The Protein Folding Problem. Annual review of biophysics. 2008-06-09, roč. 37, s. 289–316. PMID 18573083 PMCID: PMC2443096. Dostupné online [cit. 2021-04-14]. ISSN 1936-122X. DOI 10.1146/annurev.biophys.37.092707.153558. PMID 18573083.
TORRISI, Mirko; POLLASTRI, Gianluca; LE, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-22, roč. 18, s. 1301–1310. PMID 32612753 PMCID: PMC7305407. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. PMID 32612753.
ZWANZIG, R.; SZABO, A.; BAGCHI, B. Levinthal's paradox. Proceedings of the National Academy of Sciences. 1992-01-01, roč. 89, čís. 1, s. 20–22. PMID 1729690. Dostupné v archivu pořízeném dne 2021-04-14. ISSN 0027-8424. DOI 10.1073/pnas.89.1.20. PMID 1729690. (anglicky) Archivováno 14. 4. 2021 na Wayback Machine.
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins: Structure, Function, and Bioinformatics. 2019, roč. 87, čís. 12, s. 1141–1148. Dostupné online [cit. 2021-04-14]. ISSN 1097-0134. DOI 10.1002/prot.25834. PMID 31602685. (anglicky)
JUMPER, John; EVANS, Richard; PRITZEL, Alexander. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-08, roč. 596, čís. 7873, s. 583–589. Dostupné online [cit. 2022-09-02]. ISSN 1476-4687. DOI 10.1038/s41586-021-03819-2. PMID 34265844. (anglicky)

occamstypewriter.org

STEPHEN. Reciprocal Space [online]. [cit. 2021-04-14]. Dostupné online. (anglicky)

sciencedirect.com

Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-01, roč. 18, s. 1301–1310. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. (anglicky)
The Levinthal paradox: yesterday and today. Folding and Design. 1997-06-01, roč. 2, s. S69–S75. Dostupné online [cit. 2021-04-14]. ISSN 1359-0278. DOI 10.1016/S1359-0278(97)00067-9. (anglicky)
Generative deep learning for macromolecular structure and dynamics. Current Opinion in Structural Biology. 2021-04-01, roč. 67, s. 170–177. Dostupné online [cit. 2021-04-14]. ISSN 0959-440X. DOI 10.1016/j.sbi.2020.11.012. (anglicky)

sciencemag.org

SERVICENOV. 30, Robert F.; 2020; AM, 10:30. ‘The game has changed.’ AI triumphs at solving protein structures. Science | AAAS [online]. 2020-11-30 [cit. 2021-04-14]. Dostupné online. (anglicky)

science.sciencemag.org

SERVICE, Robert F. ‘The game has changed.’ AI triumphs at protein folding. Science. 2020-12-04, roč. 370, čís. 6521, s. 1144–1145. PMID 33273077. Dostupné online [cit. 2021-04-14]. ISSN 0036-8075. DOI 10.1126/science.370.6521.1144. PMID 33273077. (anglicky)

skeptic.org.uk

The Skeptic [online]. 2020-12-04 [cit. 2021-04-14]. Dostupné online. (anglicky)

technologyreview.com

DeepMind’s protein-folding AI has solved a 50-year-old grand challenge of biology. MIT Technology Review [online]. [cit. 2021-04-14]. Dostupné online. (anglicky)

web.archive.org

ZWANZIG, R.; SZABO, A.; BAGCHI, B. Levinthal's paradox. Proceedings of the National Academy of Sciences. 1992-01-01, roč. 89, čís. 1, s. 20–22. PMID 1729690. Dostupné v archivu pořízeném dne 2021-04-14. ISSN 0027-8424. DOI 10.1073/pnas.89.1.20. PMID 1729690. (anglicky) Archivováno 14. 4. 2021 na Wayback Machine.

wiley.com

onlinelibrary.wiley.com

FLOWER, Thomas G.; HURLEY, James H. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. Protein Science. 2021, roč. 30, čís. 4, s. 728–734. Dostupné online [cit. 2021-04-14]. ISSN 1469-896X. DOI 10.1002/pro.4050. PMID 33625752. (anglicky)
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins: Structure, Function, and Bioinformatics. 2019, roč. 87, čís. 12, s. 1141–1148. Dostupné online [cit. 2021-04-14]. ISSN 1097-0134. DOI 10.1002/prot.25834. PMID 31602685. (anglicky)

worldcat.org

ALQURAISHI, Mohammed. AlphaFold at CASP13. Bioinformatics. 2019-05-22, roč. 35, čís. 22, s. 4862–4865. Dostupné online [cit. 2021-04-14]. ISSN 1367-4803. DOI 10.1093/bioinformatics/btz422. PMID 31116374.
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Improved protein structure prediction using potentials from deep learning. Nature. 2020-01, roč. 577, čís. 7792, s. 706–710. Dostupné online [cit. 2021-04-14]. ISSN 1476-4687. DOI 10.1038/s41586-019-1923-7. (anglicky)
FLOWER, Thomas G.; HURLEY, James H. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. Protein Science. 2021, roč. 30, čís. 4, s. 728–734. Dostupné online [cit. 2021-04-14]. ISSN 1469-896X. DOI 10.1002/pro.4050. PMID 33625752. (anglicky)
SERVICE, Robert F. ‘The game has changed.’ AI triumphs at protein folding. Science. 2020-12-04, roč. 370, čís. 6521, s. 1144–1145. PMID 33273077. Dostupné online [cit. 2021-04-14]. ISSN 0036-8075. DOI 10.1126/science.370.6521.1144. PMID 33273077. (anglicky)
DILL, Ken A.; OZKAN, S. Banu; SHELL, M. Scott. The Protein Folding Problem. Annual review of biophysics. 2008-06-09, roč. 37, s. 289–316. PMID 18573083 PMCID: PMC2443096. Dostupné online [cit. 2021-04-14]. ISSN 1936-122X. DOI 10.1146/annurev.biophys.37.092707.153558. PMID 18573083.
Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-01, roč. 18, s. 1301–1310. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. (anglicky)
TORRISI, Mirko; POLLASTRI, Gianluca; LE, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020-01-22, roč. 18, s. 1301–1310. PMID 32612753 PMCID: PMC7305407. Dostupné online [cit. 2021-04-14]. ISSN 2001-0370. DOI 10.1016/j.csbj.2019.12.011. PMID 32612753.
ZWANZIG, R.; SZABO, A.; BAGCHI, B. Levinthal's paradox. Proceedings of the National Academy of Sciences. 1992-01-01, roč. 89, čís. 1, s. 20–22. PMID 1729690. Dostupné v archivu pořízeném dne 2021-04-14. ISSN 0027-8424. DOI 10.1073/pnas.89.1.20. PMID 1729690. (anglicky) Archivováno 14. 4. 2021 na Wayback Machine.
The Levinthal paradox: yesterday and today. Folding and Design. 1997-06-01, roč. 2, s. S69–S75. Dostupné online [cit. 2021-04-14]. ISSN 1359-0278. DOI 10.1016/S1359-0278(97)00067-9. (anglicky)
Generative deep learning for macromolecular structure and dynamics. Current Opinion in Structural Biology. 2021-04-01, roč. 67, s. 170–177. Dostupné online [cit. 2021-04-14]. ISSN 0959-440X. DOI 10.1016/j.sbi.2020.11.012. (anglicky)
SINGH, Arunima. Deep learning 3D structures. Nature Methods. 2020-03, roč. 17, čís. 3, s. 249–249. Dostupné online [cit. 2021-04-14]. ISSN 1548-7105. DOI 10.1038/s41592-020-0779-y. (anglicky)
SENIOR, Andrew W.; EVANS, Richard; JUMPER, John. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins: Structure, Function, and Bioinformatics. 2019, roč. 87, čís. 12, s. 1141–1148. Dostupné online [cit. 2021-04-14]. ISSN 1097-0134. DOI 10.1002/prot.25834. PMID 31602685. (anglicky)
JUMPER, John; EVANS, Richard; PRITZEL, Alexander. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-08, roč. 596, čís. 7873, s. 583–589. Dostupné online [cit. 2022-09-02]. ISSN 1476-4687. DOI 10.1038/s41586-021-03819-2. PMID 34265844. (anglicky)
HEKKELMAN, Maarten L.; DE VRIES, Ida; JOOSTEN, Robbie P. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nature Methods. 2022-11-24, s. 1–9. Dostupné online [cit. 2022-12-10]. ISSN 1548-7105. DOI 10.1038/s41592-022-01685-y. (anglicky)