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RISS 인기검색어
- 검색키워드
- 발행기관 : Imperial College Press ; World Scientific Publishi (검색결과 2 건)
- 무료
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Inference of large-scale gene regulatory networks using regression-based network approach.
Kim, Haseong,Lee, Jae K,Park, Taesung Imperial College Press ; World Scientific Publishi 2009 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.7 No.4
<P>The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crescentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.</P>
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An improved preprocessing algorithm for haplotype inference by pure parsimony.
Choi, Mun-Ho,Kang, Seung-Ho,Lim, Hyeong-Seok Imperial College Press ; World Scientific Publishi 2014 Journal of Bioinformatics and Computational Biolog Vol.12 No.4
<P>The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with disease. Given a set of genotypes from a population, the process of recovering the haplotypes, which explain the genotypes, is called haplotype inference (HI). We propose an improved preprocessing method for solving the haplotype inference by pure parsimony (HIPP), which excludes a large amount of redundant haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. The method uses only inclusion relations between groups of haplotypes but dramatically reduces the number of candidate haplotypes; therefore, it causes the computational time and memory reduction of real HIPP solvers. The proposed method can be easily coupled with a wide range of optimization methods which consider a set of candidate haplotypes explicitly. For the simulated and well-known benchmark datasets, the experimental results show that our method coupled with a classical exact HIPP solver run much faster than the state-of-the-art solver and can solve a large number of instances that were so far unaffordable in a reasonable time.</P>
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