Yu-Ting Hsiao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yu-Ting Hsiao has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Wei-Po Lee
Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
2 shared publications - Christoph Flamm
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
1 shared publication - Ivo L. Hofacker
Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria
1 shared publication Philipp Kügler
Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
1 shared publication- Stefan Müller
Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Wien, Austria
1 shared publication - Wei Yang
Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
1 shared publication
Latest Publications
- 20 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 13 (1) , 64-75
Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks
Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design.
- 20 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 13 (1) , 64-75
Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks
Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design.
- Open Access
- 1 January 2014
- Published by Springer Nature in BMC Systems Biology
- Vol. 8 (1) , 5
Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment
To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks.
- 1 January 2014
- Published by Springer Nature in BMC Systems Biology
- Vol. 8 (1) , 5
Open AccessDesigning a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment
To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks.