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Chang Hun You
Researcher at Washington State University
Publications - 9
Citations - 286
Chang Hun You is an academic researcher from Washington State University. The author has contributed to research in topics: Biological network & Graph (abstract data type). The author has an hindex of 5, co-authored 8 publications receiving 253 citations. Previous affiliations of Chang Hun You include Carnegie Institution for Science.
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Journal ArticleDOI
Border Control—A Membrane-Linked Interactome of Arabidopsis
Alexander M. Jones,Yuan Hu Xuan,Meng Xu,Rui-Sheng Wang,Cheng-Hsun Ho,Sylvie Lalonde,Chang Hun You,Maria Sardi,Saman A. Parsa,Erika Smith-Valle,Tianying Su,Keith A. Frazer,Guillaume Pilot,Réjane Pratelli,Réjane Pratelli,Guido Grossmann,Biswa R. Acharya,Heng-Cheng Hu,Florent Villiers,Chuanli Ju,Kouji Takeda,Zhao Su,Qunfeng Dong,Sarah M. Assmann,Jin Chen,June M. Kwak,Julian I. Schroeder,Réka Albert,Seung Y. Rhee,Wolf B. Frommer +29 more
TL;DR: Using a split-ubiquitin yeast two-hybrid screen that covers a test-space of 6.4 × 106 pairs, 12,102 membrane/signaling protein interactions from Arabidopsis are identified, with several of the identified interactions fill gaps in important signal transduction chains, while others point to functions for enigmatic unknown proteins.
Proceedings ArticleDOI
Learning patterns in the dynamics of biological networks
TL;DR: The discovered graph-rewriting rules show how biological networks change over time, and the transformation rules show the repeated patterns in the structural changes.
Proceedings ArticleDOI
Graph-Based Data Mining in Dynamic Networks: Empirical Comparison of Compression-Based and Frequency-Based Subgraph Mining
TL;DR: This work proposes a dynamic graph-based relational mining approach using graph-rewriting rules to learns patterns in networks that structurally change over time, and applies this approach to biological networks to understand how the biosystemschange over time.
Learning Node Replacement Graph Grammars in Metabolic Pathways.
TL;DR: In this paper, a graph-based relational, unsupervised learning algorithm was proposed to infer node replacement graph grammar and its application to metabolic pathways, where the authors search for frequent sub-graphs and then check for overlap among the instances of the subgraphs in the input graph.
Proceedings ArticleDOI
Temporal and structural analysis of biological networks in combination with microarray data
TL;DR: This work introduces a graph-based relational learning approach using graph-rewriting rules for temporal and structural analysis of biological networks changing over time, and describes how the graphs temporally and structurally change over time in the dynamic graph representing biological networks in combination with microarray data.