Zobrazeno 1 - 10 of 66 pro vyhledávání: '"GENG, Cunliang"'
1
Report
Deep-learning enhancement of large scale numerical simulations
Autor: van Leeuwen, Caspar, Podareanu, Damian, Codreanu, Valeriu, Cai, Maxwell X., Berg, Axel, Zwart, Simon Portegies, Stoffer, Robin, Veerman, Menno, van Heerwaarden, Chiel, Otten, Sydney, Caron, Sascha, Geng, Cunliang, Ambrosetti, Francesco, Bonvin, Alexandre M. J. J.
Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large models, but limits in performance increase that have become more prominen
Externí odkaz: http://arxiv.org/abs/2004.03454
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2
Akademický článek
MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations.
Autor: Jung, Yong1,2,3 (AUTHOR), Geng, Cunliang4 (AUTHOR), Bonvin, Alexandre M. J. J.4 (AUTHOR), Xue, Li C.4,5 (AUTHOR) li.xue@radboudumc.nl, Honavar, Vasant G.1,2,3,6,7,8,9 (AUTHOR) li.xue@radboudumc.nl
Publikováno v: Biomolecules (2218-273X). Jan2023, Vol. 13 Issue 1, p121. 20p.
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3
NPLinker eScience Workshop on Natural Product Genome & Metabolome Mining Materials
Autor: Van Der Hooft, Justin J.J., Medema, Marnix H., Weber, Tilmann, Leao, Tiago, Navarro-Muñoz, Jorge C., Schmid, Robin, Brungs, Corinna, Zdouc, Mitja M., Terlouw, Barbara, Barona-Gómez, Paco, Geng, Cunliang, Caesar, Lindsay
These materials were used as a part of the NPLinker eScience Workshop on Natural Product Genome & Metabolome Mining: https://www.wur.nl/en/research-results/chair-groups/plant-sciences/bioinformatics/teaching/nplinker_workshop.htm Most materials are a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::22f09dc704217034e26fec41bed3f5f2
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4
Using Deep Learning in Stroke Rehabilitation - A step towards individualized patient care
Autor: David, Sina, Georgievska, Sonja, Liu, Yang, Geng, Cunliang, Punt, Michiel
Publikováno v: Deutsche Gesellschaft für Biomechanik
David, S, Georgievska, S, Liu, Y, Geng, C & Punt, M 2022, ' Using Deep Learning in Stroke Rehabilitation-A step towards individualized patient care ', Paper presented at Deutsche Gesellschaft für Biomechanik, Cologne, Germany, 28/09/22-30/09/22 .
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::95fad68028fd398fa13dbec2850a6676
https://hdl.handle.net/1871.1/1b737aa3-5402-4396-9330-36c2bd8df994
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6
Akademický článek
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7
Akademický článek
Beyond Docker: Enhancing vantage6 with Kubernetes for Federated Learning.
Autor: CADAVID, Héctor, GENG, Cunliang
Publikováno v: Studies in Health Technology & Informatics; 2024, Vol. 316, p1603-1604, 2p
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8
Akademický článek
GameBus on FHIR.
Autor: GENG, Cunliang, KHANSHAN, Alireza, Van GORP, Pieter
Publikováno v: Studies in Health Technology & Informatics; 2024, Vol. 316, p1312-1313, 2p
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9
iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines
Autor: Renaud, Nicolas, Jung, Yong, Honavar, Vasant, Geng, Cunliang, Bonvin, Alexandre M.J.J., Xue, Li C., Sub NMR Spectroscopy, NMR Spectroscopy
Publikováno v: SoftwareX, 11. Elsevier BV
SoftwareX, 11
SoftwareX, Vol 11, Iss, Pp-(2020)
Contains fulltext : 220863.pdf (Publisher’s version ) (Open Access) Computational docking is a promising tool to model three-dimensional (3D) structures of protein–protein complexes, which provides fundamental insights of protein functions in the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39dc7047a246cb9673952504107c1ced
https://hdl.handle.net/1874/396533
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10
An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45
Autor: Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J.
Our information‐driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its s
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______101::f154be9aa09d08aacf0b80c020caa1d7
https://dspace.library.uu.nl/handle/1874/390638
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