The Experts below are selected from a list of 56667 Experts worldwide ranked by ideXlab platform
Hsuehwei Chang - One of the best experts on this subject based on the ideXlab platform.
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protein folding prediction in the hp model using ions motion optimization with a Greedy Algorithm
Biodata Mining, 2018Co-Authors: Chenghong Yang, Kuochuan Wu, Liyeh Chuang, Hsuehwei ChangAbstract:The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity. In this study, the ions motion optimization (IMO) Algorithm was combined with the Greedy Algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (Greedy Algorithm) to the new Algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction. Overall, the HP model integrated with IMO and a Greedy Algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.
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Protein folding prediction in the HP model using ions motion optimization with a Greedy Algorithm
BioData Mining, 2018Co-Authors: Chenghong Yang, Liyeh Chuang, Yu-shiun Lin, Hsuehwei ChangAbstract:Background The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity. Results In this study, the ions motion optimization (IMO) Algorithm was combined with the Greedy Algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (Greedy Algorithm) to the new Algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction. Conclusion Overall, the HP model integrated with IMO and a Greedy Algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.
K Chen - One of the best experts on this subject based on the ideXlab platform.
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wind turbine layout optimization with multiple hub height wind turbines using Greedy Algorithm
Renewable Energy, 2016Co-Authors: K Chen, Mengxuan Song, Xu Zhang, Shuping WangAbstract:Abstract Wind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using Greedy Algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional Greedy Algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed Greedy Algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed Greedy Algorithm than the one using genetic Algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional Greedy Algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.
Shuping Wang - One of the best experts on this subject based on the ideXlab platform.
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wind turbine layout optimization with multiple hub height wind turbines using Greedy Algorithm
Renewable Energy, 2016Co-Authors: K Chen, Mengxuan Song, Xu Zhang, Shuping WangAbstract:Abstract Wind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using Greedy Algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional Greedy Algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed Greedy Algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed Greedy Algorithm than the one using genetic Algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional Greedy Algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.
Philippe Derreumaux - One of the best experts on this subject based on the ideXlab platform.
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Improved Greedy Algorithm for protein structure reconstruction.
Journal of computational chemistry, 2005Co-Authors: Pierre Tufféry, Frédéric Guyon, Philippe DerreumauxAbstract:This article concerns the development of an improved Greedy Algorithm for protein structure reconstruction. Our stochastic Greedy Algorithm, which attempts to locate the ground state of an approximate energy function, exploits the fact that protein structures consist of overlapping structural building blocks that are not independent. Application of this approach to a series of 16 proteins with 50-250 amino acids leads to predicted models deviating from the experimental structures by 0.5 A RMSD using an RMSD-based energy function and within 1.5 to 4.8 A RMSD using a Go-based energy function. The Go-based results are significant because they illustrate the strength of combining structural fragments and stochastic Greedy Algorithms in capturing the native structures of proteins stabilized by long-range interactions separated by more than 30 amino acids. These results clearly open the door to less computationally demanding solutions to predict structures from sequences.
Ohno-machadolucila - One of the best experts on this subject based on the ideXlab platform.
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A Greedy Algorithm for supervised discretization
Journal of Biomedical Informatics, 2004Co-Authors: Butterworthrichard, A Simovicidan, S Santosgustavo, Ohno-machadolucilaAbstract:We present a Greedy Algorithm for supervised discretization using a metric defined on the space of partitions of a set of objects. This proposed technique is useful for preparing the data for class...
