A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features
Trabajo presentado en el 45 Congreso de la Sociedad Española de Bioquímica y Biología Molecular (SEBBM2023), celebrado en Zaragoza (España), del 5 al 8 de septiembre de 2023
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2023-09-05
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dig-icvv-es-10261-3534392024-04-10T12:47:57Z A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features Rodríguez-Lumbreras, Luis A. Glaser, Fabian Fernández-Recio, Juan Trabajo presentado en el 45 Congreso de la Sociedad Española de Bioquímica y Biología Molecular (SEBBM2023), celebrado en Zaragoza (España), del 5 al 8 de septiembre de 2023 2024-04-10T12:45:54Z 2024-04-10T12:45:54Z 2023-09-05 2024-04-10T12:45:55Z comunicación de congreso 45 Congreso de la Sociedad Española de Bioquímica y Biología Molecular - SEBBM (2023) http://hdl.handle.net/10261/353439 Sí none |
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| description |
Trabajo presentado en el 45 Congreso de la Sociedad Española de Bioquímica y Biología Molecular (SEBBM2023), celebrado en Zaragoza (España), del 5 al 8 de septiembre de 2023 |
| format |
comunicación de congreso |
| author |
Rodríguez-Lumbreras, Luis A. Glaser, Fabian Fernández-Recio, Juan |
| spellingShingle |
Rodríguez-Lumbreras, Luis A. Glaser, Fabian Fernández-Recio, Juan A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| author_facet |
Rodríguez-Lumbreras, Luis A. Glaser, Fabian Fernández-Recio, Juan |
| author_sort |
Rodríguez-Lumbreras, Luis A. |
| title |
A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| title_short |
A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| title_full |
A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| title_fullStr |
A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| title_full_unstemmed |
A machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| title_sort |
machine learning method to estimate the impact of mutations on protein binding affinity based on physicochemical, statistical and evolutionary conservation features |
| publishDate |
2023-09-05 |
| url |
http://hdl.handle.net/10261/353439 |
| work_keys_str_mv |
AT rodriguezlumbrerasluisa amachinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures AT glaserfabian amachinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures AT fernandezreciojuan amachinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures AT rodriguezlumbrerasluisa machinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures AT glaserfabian machinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures AT fernandezreciojuan machinelearningmethodtoestimatetheimpactofmutationsonproteinbindingaffinitybasedonphysicochemicalstatisticalandevolutionaryconservationfeatures |
| _version_ |
1798164480001048576 |