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Arian R Jamasb

Showing results (1-10 of 10) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|July 8, 2021
Deep Learning for Protein-Protein Interaction Site PredictionArian R Jamasb, Ben Day, Cătălina Cangea, et al.
Plos Biology|October 20, 2017
Ethoscopes: An open platform for high-throughput ethomicsQuentin Geissmann, Luis Garcia Rodriguez, Esteban J Beckwith, et al.
Biorxiv : the Preprint Server for Biology|June 3, 2024
gRNAde: Geometric Deep Learning for 3D RNA inverse designChaitanya K Joshi, Arian R Jamasb, Ramon Viñas, et al.
Arxiv|June 3, 2024
gRNAde: Geometric Deep Learning for 3D RNA inverse designChaitanya K Joshi, Arian R Jamasb, Ramon Viñas, et al.
Arxiv|July 1, 2024
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone DesignRishabh Anand, Chaitanya K Joshi, Alex Morehead, et al.
Arxiv|July 1, 2024
Evaluating Representation Learning on the Protein Structure UniverseArian R Jamasb, Alex Morehead, Chaitanya K Joshi, et al.
Briefings in Bioinformatics|January 8, 2021
SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targetsAli F Alsulami, Sherine E Thomas, Arian R Jamasb, et al.
Chemical Science|December 12, 2022
Data-driven discovery of molecular photoswitches with multioutput Gaussian processesRyan-Rhys Griffiths, Jake L Greenfield, Aditya R Thawani, et al.
Computational and Structural Biotechnology Journal|July 8, 2021
Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronavirusesChristopher A Beaudoin, Arian R Jamasb, Ali F Alsulami, et al.
Briefings in Bioinformatics|May 20, 2021
Utilizing graph machine learning within drug discovery and developmentThomas Gaudelet, Ben Day, Arian R Jamasb, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Methods in Molecular Biology (Clifton, N.J.)|July 8, 2021
Deep Learning for Protein-Protein Interaction Site PredictionArian R Jamasb, Ben Day, Cătălina Cangea, et al.
Plos Biology|October 20, 2017
Ethoscopes: An open platform for high-throughput ethomicsQuentin Geissmann, Luis Garcia Rodriguez, Esteban J Beckwith, et al.
Biorxiv : the Preprint Server for Biology|June 3, 2024
gRNAde: Geometric Deep Learning for 3D RNA inverse designChaitanya K Joshi, Arian R Jamasb, Ramon Viñas, et al.
Arxiv|June 3, 2024
gRNAde: Geometric Deep Learning for 3D RNA inverse designChaitanya K Joshi, Arian R Jamasb, Ramon Viñas, et al.
Arxiv|July 1, 2024
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone DesignRishabh Anand, Chaitanya K Joshi, Alex Morehead, et al.
Arxiv|July 1, 2024
Evaluating Representation Learning on the Protein Structure UniverseArian R Jamasb, Alex Morehead, Chaitanya K Joshi, et al.
Briefings in Bioinformatics|January 8, 2021
SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targetsAli F Alsulami, Sherine E Thomas, Arian R Jamasb, et al.
Chemical Science|December 12, 2022
Data-driven discovery of molecular photoswitches with multioutput Gaussian processesRyan-Rhys Griffiths, Jake L Greenfield, Aditya R Thawani, et al.
Computational and Structural Biotechnology Journal|July 8, 2021
Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronavirusesChristopher A Beaudoin, Arian R Jamasb, Ali F Alsulami, et al.
Briefings in Bioinformatics|May 20, 2021
Utilizing graph machine learning within drug discovery and developmentThomas Gaudelet, Ben Day, Arian R Jamasb, et al.
Pageof 1