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 Online-Ressource | |
| Verfasst von: | Yang, Mi [VerfasserIn]  |
| Sáez Rodríguez, Julio [VerfasserIn] | |
| Titel: | Community assessment of the predictability of cancer protein and phosphoprotein levels from genomics and transcriptomics |
| Verf.angabe: | Mi Yang, Francesca Petralia, Zhi Li, Hongyang Li, Weiping Ma, Xiaoyu Song, Sunkyu Kim, Heewon Lee, Han Yu, Bora Lee, Seohui Bae, Eunji Heo, Jan Kaczmarczyk, Piotr Stępniak, Michał Warchoł, Thomas Yu, Anna P. Calinawan, Paul C. Boutros, Samuel H. Payne, Boris Reva, NCI-CPTAC-DREAM Consortium, Emily Boja, Henry Rodriguez, Gustavo Stolovitzky, Yuanfang Guan, Jaewoo Kang, Pei Wang, David Fenyö, and Julio Saez-Rodriguez |
| E-Jahr: | 2020 |
| Jahr: | [August 26, 2020] |
| Umfang: | 19 S. |
| Illustrationen: | Illustrationen |
| Fussnoten: | The members of the NCI-CPTAC-DREAM Consortium are Tunde Aderinwale, Ebrahim Afyounian, Piyush Agrawal, Mehreen Ali, Alicia Amadoz, Francisco Azuaje, John Bachman, Seohui Bae, Sherry Bhalla, José Carbonell-Caballero, Priyanka Chakraborty, Kumardeep Chaudhary, Yonghwa Choi, Yoonjung Choi, Cankut Çubuk, Sandeep Kumar Dhanda, Joaquín Dopazo, Laura L. Elo, Ábel Fóthi, Olivier Gevaert, Kirsi Granberg, Russell Greiner, Eunji Heo, Marta R. Hidalgo, Vivek Jayaswal, Hwisang Jeon, Minji Jeon, Sunil V. Kalmady, Yasuhiro Kambara, Jaewoo Kang, Keunsoo Kang, Tony Kaoma, Harpreet Kaur, Hilal Kazan, Devishi Kesar, Juha Kesseli, Daehan Kim, Keonwoo Kim, Sang-Yoon Kim, Sunkyu Kim, Sajal Kumar, Bora Lee, Heewon Lee, Yunpeng Liu, Roland Luethy, Swapnil Mahajan, Mehrad Mahmoudian, Arnaud Muller, Petr V. Nazarov, Hien Nguyen, Matti Nykter, Shujiro Okuda, Sungsoo Park, Gajendra Pal Singh Raghava, Jagath C. Rajapakse, Tommi Rantapero, Hobin Ryu, Francisco Salavert, Sohrab Saraei, Ruby Sharma, Ari Siitonen, Artem Sokolov, Kartik Subramanian, Veronika Suni, Tomi Suomi, Léon-Charles Tranchevent, Salman Sadullah Usmani, Tommi Välikangas, Roberto Vega, and Hua Zhong ; Gesehen am 29.09.2020 |
| Titel Quelle: | Enthalten in: Cell systems |
| Ort Quelle: | Maryland Heights, MO : Elsevier, 2015 |
| Jahr Quelle: | 2020 |
| Band/Heft Quelle: | 11(2020,2) Seite 186-195, e1-e9 |
| ISSN Quelle: | 2405-4720 |
| Abstract: | Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper’s transparent peer review process is included in the Supplemental Information. |
| DOI: | doi:10.1016/j.cels.2020.06.013 |
| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt. Volltext: https://doi.org/10.1016/j.cels.2020.06.013 |
| Volltext: http://www.sciencedirect.com/science/article/pii/S2405471220302428 | |
| DOI: https://doi.org/10.1016/j.cels.2020.06.013 | |
| Datenträger: | Online-Ressource |
| Sprache: | eng |
| Sach-SW: | cancer |
| crowdsourcing | |
| genomics | |
| machine learning | |
| protein regulation | |
| proteogenomics | |
| proteomics | |
| K10plus-PPN: | 1733923497 |
| Verknüpfungen: | → Zeitschrift |
