L
Liang Liang
Researcher at University of Miami
Publications - 56
Citations - 2018
Liang Liang is an academic researcher from University of Miami. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 14, co-authored 47 publications receiving 1381 citations. Previous affiliations of Liang Liang include Max Planck Society & Georgia Institute of Technology.
Papers
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Journal ArticleDOI
Objective comparison of particle tracking methods
Nicolas Chenouard,Ihor Smal,Fabrice de Chaumont,Martin Maška,Martin Maška,Ivo F. Sbalzarini,Yuanhao Gong,Janick Cardinale,Craig Carthel,Stefano Coraluppi,Mark R. Winter,Andrew R. Cohen,William J. Godinez,Karl Rohr,Yannis Kalaidzidis,Liang Liang,James S. Duncan,Hongying Shen,Yingke Xu,Klas E. G. Magnusson,Joakim Jalden,Helen M. Blau,Perrine Paul-Gilloteaux,Philippe Roudot,Charles Kervrann,François Waharte,Jean-Yves Tinevez,Spencer L. Shorte,Joost Willemse,Katherine Celler,Gilles P. van Wezel,Han-Wei Dan,Yuh-Show Tsai,Carlos Ortiz de Solórzano,Jean-Christophe Olivo-Marin,Erik Meijering +35 more
TL;DR: Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
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A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
TL;DR: This study marks, to the authors' knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.
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A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm
TL;DR: This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA-predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues.
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A role of OCRL in clathrin-coated pit dynamics and uncoating revealed by studies of Lowe syndrome cells
Ramiro Nandez,Daniel M. Balkin,Mirko Messa,Liang Liang,Summer Paradise,Heather Czapla,Marco Y. Hein,James S. Duncan,Matthias Mann,Pietro De Camilli +9 more
TL;DR: It is shown that OCRL loss in Lowe syndrome patient fibroblasts impacts clathrin-mediated endocytosis and results in an endocytic defect, and that defects in clathin- mediated endocyTosis likely contribute to pathology in patients with OCRL mutations.
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A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta.
Liang Liang,Wenbin Mao,Wei Sun +2 more
TL;DR: Deep neural networks are developed to directly estimate the steady-state distributions of pressure and flow velocity inside the thoracic aorta by using machine learning techniques to demonstrate the feasibility and great potential of using DNNs as a fast and accurate surrogate model for hemodynamic analysis of large blood vessels.