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RISS 인기검색어
- 검색키워드
- 저자 : Junliang Shang (검색결과 3 건)
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Incorporating heuristic information into ant colony optimization for epistasis detection
Junliang Shang,Junying Zhang,Xiujuan Lei,Yuanyuan Zhang,Baodi Chen 한국유전학회 2012 Genes & Genomics Vol.34 No.3
Epistasis has been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for epistasis detection, genome-wide association study remains a challenging task: it makes the search space excessively huge while solution quality is excessively demanded. In this study, we introduce an ant colony optimization based algorithm,AntMiner, by incorporating heuristic information into ant-decision rules. The heuristic information is used to direct ants in the search process for improving computational efficiency and solution accuracy. During iterations, chi-squared test is conducted to measure the association between an interaction and the phenotype. At the completion of the iteration process, statistically significant epistatic interactions are ordered and then screened by a post-procedure. Experiments of AntMiner and its comparison with existing algorithms epiMODE, TEAM and AntEpiSeeker are performed on both simulation data sets and real age-related macular degeneration data set, under the criteria of detection power and sensitivity. Results demonstrate that AntMiner is promising for epistasis detection. In terms of detection power, AntMiner performs best among all the other algorithms on all cases regardless of epistasis models and single nucleotide polymorphism size; compared with AntEpiSeeker, AntMiner can obtain better detection power but with less ants and iterations. In terms of sensitivity, AntMiner is better than AntEpiSeeker in detecting epistasis models displaying marginal effects but it has moderate sensitivity on epistasis models displaying no marginal effects. The study may provide clues on heuristics for further epistasis detection. The software package is available online at https://sourceforge.net/projects/antminer/files/.
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Junliang Shang,Junying Zhang,Xiujuan Lei,Wenying Zhao,Yafei Dong 한국유전학회 2013 Genes & Genomics Vol.35 No.3
Epistasis is a ubiquitous phenomenon in genetics,and is considered to be one of the main factors in current efforts to detect missing heritability for complex diseases. Simulation is a critical tool in developing methodologies that can more effectively detect and study epistasis. Here we present a simulator, epiSIM (epistasis SIMulator), that can simulate some of the statistical properties of genetic data. EpiSIM is capable of expanding the range of the epistasis models that current simulators offer, including epistasis models that display marginal effects and those that display no marginal effects. One or more of these epistasis models can be embedded simultaneously into a single simulation data set, jointly determining the phenotype. In addition,epiSIM is independent of any outside data source in generating linkage disequilibrium patterns and haplotype blocks. We demonstrate the wide applicability of epiSIM by performing several data simulations, and examine its properties by comparing it with current representative simulators and by comparing the data that it generates with real data. Our experiments demonstrate that epiSIM is a valuable addition and a nice complement to the existing epistasis simulators. The software package is available online at https://sourceforge.net/projects/episimsimulator/files/.
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Shasha Yuan,Jinxing Liu,Junliang Shang,Xiangzhen Kong,Qi Yuan,Zhen Ma 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.4
Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of greatsignificance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patientspecificseizure detection applying the earth mover’s distance (EMD) in scalp EEG. Firstly, the wavelet decomposition isexecuted to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms andafterwards the distances between histograms in pairs are computed applying the earth mover’s distance as effectivefeatures. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) forclassification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. Toevaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% andfalse detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizuremonitoring in clinical practice.
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