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State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast., and . IJDMB, 1 (1): 77-87 (2006)OVarCall: Bayesian Mutation Calling Method Utilizing Overlapping Paired-End Reads., , , , , and . ISBRA, volume 9683 of Lecture Notes in Computer Science, page 40-51. Springer, (2016)Poster: Comprehensive pharmacogenomic pathway screening by data assimilation., , , , and . ICCABS, page 246. IEEE Computer Society, (2011)Estimation of macroscopic parameter in agent-based pandemic simulation., , , , and . FUSION, page 1-6. IEEE, (2011)Identifying Hidden Confounders in Gene Networks by Bayesian Networks., , , , , and . BIBE, page 168-173. IEEE Computer Society, (2010)Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models., , , , , , , and . Bioinform., 24 (7): 932-942 (2008)A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models., , , , , , , and . Comput. Stat. Data Anal., (2016)Parallel Agent-Based Simulator for Influenza Pandemic., , , , and . AAMAS Workshops, volume 7068 of Lecture Notes in Computer Science, page 361-370. Springer, (2011)Reconstruction of high read-depth signals from low-depth whole genome sequencing data using deep learning., , , and . BIBM, page 1227-1232. IEEE Computer Society, (2017)Network profiling analysis for generating modulator-dependent gene networks., , , , , and . BIBM Workshops, page 836. IEEE, (2010)