Yung-Keun Kwon

Korea Advanced Institute of Science and Technology , Seoul, Seoul, South Korea

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Publications (21)48.13 Total impact

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    ABSTRACT: The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network's kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.
    Science Signaling 01/2011; 4(175):ra35. · 7.65 Impact Factor
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    Dongsan Kim, Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: An incoherent feed-forward loop (FFL) is one of the most-frequently observed motifs in biomolecular regulatory networks. It has been thought that the incoherent FFL is designed simply to induce a transient response shaped by a 'fast activation and delayed inhibition'. We find that the dynamics of various incoherent FFLs can be further classified into two types: time-dependent biphasic responses and dose-dependent biphasic responses. Why do the structurally identical incoherent FFLs play such different dynamical roles? Through computational studies, we show that the dynamics of the two types of incoherent FFLs are mutually exclusive. Following from further computational results and experimental observations, we hypothesize that incoherent FFLs have been optimally designed to achieve distinct biological function arising from different cellular contexts. Additional Supporting Information may be found in the online version of the article.
    BioEssays 11/2008; 30(11-12):1204-11. · 5.42 Impact Factor
  • Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: MOTIVATION: It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together. RESULTS: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness.
    Bioinformatics 08/2008; 24(17):1926-32. · 5.47 Impact Factor
  • Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: MOTIVATION: It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now. RESULTS: Through extensive computational experiments, we found that networks with a larger number of positive feedback loops and a smaller number of negative feedback loops are likely to be more robust against perturbations. Moreover, we found that the nodes of a robust network subject to perturbations are mostly involved with a smaller number of feedback loops compared with the other nodes not usually subject to perturbations. This topological characteristic eventually makes the robust network fragile against unexpected mutations at the nodes not previously exposed to perturbations.
    Bioinformatics 05/2008; 24(7):987-94. · 5.47 Impact Factor
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    Sung-Soon Choi, Yung-Keun Kwon, Byung-Ro Moon
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    ABSTRACT: The properties of symmetric fitness functions are investigated. We show that the search spaces obtained from symmetric functions have the zero-correlation structures between fitness and distance. It is also proven that symmetric functions induce a class of the hardest problems in terms of the epistasis variance and its variants. These analyses suggest that the existing quantitative measures cannot discriminate among symmetric functions, which reveals critical limitations of the measures. To take a closer look at the symmetric functions, additional analyses are performed from other viewpoints including additive separability and boundedness. It is shown that additive separability in a symmetric function is closely related to the symmetry of its subfunctions. This elucidates why most of the well-known symmetric fitness functions are additively inseparable. The properties of two-bounded symmetric functions are investigated and they are utilized in designing an efficient algorithm to check additive separability for the two-bounded functions. Throughout this paper, we heavily use the generalized Walsh transform over multary alphabets. Our results have an independent interest as a nontrivial application of the generalized Walsh analysis.
    IEEE Transactions on Evolutionary Computation 01/2008; · 4.81 Impact Factor
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    Yung-Keun Kwon, Byung-Ro Moon
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    ABSTRACT: In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction.
    IEEE Transactions on Neural Networks 06/2007; 18(3):851-64. · 2.95 Impact Factor
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    Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: Boolean networks have been frequently used to study the dynamics of biological networks. In particular, there have been various studies showing that the network connectivity and the update rule of logical functions affect the dynamics of Boolean networks. There has been, however, relatively little attention paid to the dynamical role of a feedback loop, which is a circular chain of interactions between Boolean variables. We note that such feedback loops are ubiquitously found in various biological systems as multiple coupled structures and they are often the primary cause of complex dynamics. In this article, we investigate the relationship between the multiple coupled feedback loops and the dynamics of Boolean networks. We show that networks have a larger proportion of basins corresponding to fixed-point attractors as they have more coupled positive feedback loops, and a larger proportion of basins for limit-cycle attractors as they have more coupled negative feedback loops.
    Biophysical Journal 05/2007; 92(8):2975-81. · 3.67 Impact Factor
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    Dongsan Kim, Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: Cellular circuits have positive and negative feedback loops that allow them to respond properly to noisy external stimuli. It is intriguing that such feedback loops exist in many cases in a particular form of coupled positive and negative feedback loops with different time delays. As a result of our mathematical simulations and investigations into various experimental evidences, we found that such coupled feedback circuits can rapidly turn on a reaction to a proper stimulus, robustly maintain its status, and immediately turn off the reaction when the stimulus disappears. In other words, coupled feedback loops enable cellular systems to produce perfect responses to noisy stimuli with respect to signal duration and amplitude. This suggests that coupled positive and negative feedback loops form essential signal transduction motifs in cellular signaling systems.
    BioEssays 02/2007; 29(1):85-90. · 5.42 Impact Factor
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    Yung-Keun Kwon, Kwang-Hyun Cho
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    ABSTRACT: Many biological networks such as protein-protein interaction networks, signaling networks, and metabolic networks have topological characteristics of a scale-free degree distribution. Preferential attachment has been considered as the most plausible evolutionary growth model to explain this topological property. Although various studies have been undertaken to investigate the structural characteristics of a network obtained using this growth model, its dynamical characteristics have received relatively less attention. In this paper, we focus on the robustness of a network that is acquired during its evolutionary process. Through simulations using Boolean network models, we found that preferential attachment increases the number of coupled feedback loops in the course of network evolution. Whereas, if networks evolve to have more coupled feedback loops rather than following preferential attachment, the resulting networks are more robust than those obtained through preferential attachment, although both of them have similar degree distributions. The presented analysis demonstrates that coupled feedback loops may play an important role in network evolution to acquire robustness. The result also provides a hint as to why various biological networks have evolved to contain a number of coupled feedback loops.
    BMC Bioinformatics 02/2007; 8:430. · 3.02 Impact Factor
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    Yung-Keun Kwon, Sun Shim Choi, Kwang-Hyun Cho
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    ABSTRACT: A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks. We discovered that the number of feedback loops (NuFBL) is a crucial measure for evaluating the importance of a network node and verified this through a signal transduction network in the hippocampal CA1 neuron of mice as well as through generalized biological network models represented by Boolean networks. In particular, we observed that the proteins with a larger NuFBL are more likely to be essential and to evolve slowly in the hippocampal CA1 neuronal signal transduction network. Then, from extensive simulations based on the Boolean network models, we proved that a network node with the larger NuFBL is likely to be more important as the mutations of the initial state or the update rule of such a node made the network converge to a different attractor. These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics. The presented analysis on topological characteristics of biological networks showed that the number of feedback loops is positively correlated with the functional importance of network nodes. This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.
    BMC Bioinformatics 02/2007; 8:384. · 3.02 Impact Factor
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    Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon
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    ABSTRACT: Genetic programming has been considered a promising ap- proach for function approximation since it is possible to op- timize both the functional form and the coefficients. How- ever, it is not easy to find an optimal set of coefficients by using only non-adjustable constant nodes in genetic pro- gramming. To overcome the problem, there have been some studies on genetic programming using adjustable parame- ters in linear or nonlinear models. Although the nonlinear parametric model has a merit over the linear parametric model, there have been few studies on it. In this paper, we propose a nonlinear parametric genetic programming which uses a nonlinear gradient method to estimate parameters. The most notable feature in the proposed genetic program- ming is that we design a parameter attachment algorithm using as few redundant parameters as possible.
    Genetic and Evolutionary Computation Conference, GECCO 2006, Proceedings, Seattle, Washington, USA, July 8-12, 2006; 01/2006
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    Yung-Keun Kwon, Byung-Ro Moon, Sung-Deok Hong
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    ABSTRACT: Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenberg-Marquardt algorithm of parametric model. We designed the chromosomes in a way that geographically exploits the relationships between parameters. The latter one is another GA of nonparametric model that is combined with a feedforward neural network. The neuro-genetic hybrid here differs from others in that it evolves diverse input features instead of connection weights. We tested the two GAs with the problem of finding a better critical heat flux (CHF) function of nuclear fuel bundle which is directly related to the nuclear-reactor thermal margin and operation. The experimental result improved the existing CHF function originated from the KRB-1 CHF correlation at the Korea Atomic Energy Research Institute (KAERI) and achieved the correlation uncertainty reduction of 15.4% that would notably contribute to increasing the thermal margin of the nuclear power plants.
    IEEE Transactions on Nuclear Science 05/2005; · 1.22 Impact Factor
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    Yung-Keun Kwon, Byung Ro Moon
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    ABSTRACT: Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure optimization in artificial neural networks. There were many nonlinear feature extraction methods using neural networks but they still have the same difficulties arisen from the fixed network topology. In this paper, we propose a novel combination of genetic algorithm and feedforward neural networks for nonlinear feature extraction. The genetic algorithm evolves the feature space by utilizing characteristics of hidden neurons. It improved remarkably the performance of neural networks on a number of real world regression and classification problems.
    Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25-29, 2005; 01/2005
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    Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon
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    ABSTRACT: In this paper, we propose a neuro-genetic stock prediction system based on financial correlation between companies. A number of input variables are produced from the relatively highly correlated companies. The genetic algorithm selects a set of informative input features among them for a recurrent neural network. It showed notable improvement over not only the buy-and-hold strategy but also the recurrent neural network using only the input variables from the target company.
    Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25-29, 2005; 01/2005
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    Yung-Keun Kwon, Byung Ro Moon
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    ABSTRACT: We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable im- provement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.
    Genetic and Evolutionary Computation - GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004, Proceedings, Part II; 01/2004
  • Yung-Keun Kwon, Byung Ro Moon
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    ABSTRACT: Given N data pairs {Xi, yi}, i=1,2,...,N, where each Xi is an n-dimensional vector of independent variables $(X_i = )$(X_i = ) and yi is a dependent variable, the function approximation problem (FAP) is finding a function that best explains the N pairs of Xi and yi. From the universal approximation theorem and inherent approximation capabilities proved by various researches, artificial neural networks (ANNs) are considered as powerful function approximators. There are two main issues on the feedforward neural networks’ performance. One is to determine its structure. The other issue is to specify the weights of a network that minimizes its error. Genetic algorithm (GA) is a global search technique and is useful for complex optimization problems. So, it has been considered to have potential to reinforce the performance of neural networks. Many researchers tried to optimize the weights of networks using genetic algorithms alone or combined with the backpropagation algorithm. Others also tried to find a good topology that is even more difficult and called a “black art.”
    Genetic and Evolutionary Computation - GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004, Proceedings, Part II; 01/2004
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    Yong-Hyuk Kim, Yung-Keun Kwon, Byung Ro Moon
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    ABSTRACT: As a preprocessing for genetic algorithms, static reorder- ing helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algo- rithms. In this paper, we propose a static reordering method indepen- dent of problem-specific knowledge. One of the novel features of our reordering method is that it is applicable to any problem with no infor- mation about the problem. The proposed method constructs a weighted complete graph from the gene distances calculated from solutions with relatively high fitnesses, transforms them into a gene-interaction graph, and finds a gene rearrangement. Extensive experimental results showed significant improvement for a number of applications.
    Genetic and Evolutionary Computation - GECCO 2003, Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003. Proceedings, Part I; 01/2003
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    Yung-Keun Kwon, Byung Ro Moon
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    ABSTRACT: We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.
    Genetic and Evolutionary Computation - GECCO 2003, Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003. Proceedings, Part II; 01/2003
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    ABSTRACT: It is difficult for teachers to find out copies in their program assignments. We propose a method which detects the similarity of assignments. We first transform a program into a sequence of tokens and provide it to an artificial neural network. The neural network is optimized by a hybrid genetic algorithm. Experimental results showed considerable improvement on artificial neural networks.
    12/2002: pages 208-208;
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    Yung-Keun Kwon, Sung-Deok Hong, Byung Ro Moon
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    ABSTRACT: Function approximation is the problem of nding a function that best explains the relationship between independent variables and a dependent variable. We propose a genetic hybrid for the critical heat ux func-tion approximation which critically a ects the performance of nuclear plants. The problem is represented for genetic algorithm in a way that exploits the relationships be-tween parameters. The experimental result signi cantly improved the existing function at KAERI (Korea Atomic Energy Research Institute). The framework is not just for the tested problem; it is believed to be applicable to other function approximation problems.
    GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002; 01/2002

Publication Stats

287 Citations
48.13 Total Impact Points

Institutions

  • 2007–2011
    • Korea Advanced Institute of Science and Technology
      • Department of Bio and Brain Engineering
      Seoul, Seoul, South Korea
  • 2008
    • University of Ulsan
      • Department of Electrical Engineering
      Ulsan, Ulsan, South Korea
  • 2003–2008
    • Seoul National University
      • • School of Computer Science and Engineering
      • • Department of Computer Science and Engineering
      Seoul, Seoul, South Korea