Wooyoung Kim

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Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused(More)
Finding network motifs in biological networks is a computationally intensive task as it involves traversing through a large network to enumerate all possible sub graphs of a given size, and then determining their statistical uniqueness by sampling sub graphs from a large number (more than 1,000) of random graph pools. There have been parallelization efforts(More)
0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.04.133 q This document is a collaborative effort. ⇑ Corresponding authors. Tel.: +1 404 413 5703. E-mail addresses: wkim@cs.gsu.edu (W. Kim), jingu@cc.gatech.edu (J. Kim), pan@cs.gsu.edu (Y. Pa Park). The problem of discovering motifs from protein sequences is a critical and(More)
Most graph algorithms are challenging in parallelization, in particular executing fine-grain computation at each graph node in parallel from both programmability and performance viewpoints. To bridge the semantic gap between the original sequential algorithms and their corresponding parallelized programs, we have been developing MASS: a parallel library for(More)
Epitomic analysis, a recent statistical approach to form a generative model, has been applied to image, video and audio processing applications. We apply the epitomic analysis to motion capture data and define it as a motion epitome, a probabilistic model representing a finite set of primitive movements which retain various lengths of local dynamics. We(More)