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- 저자 : Jiajia Hou (검색결과 4 건)
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Xinyan Xie,Fang Hou,Li Li,Yanlin Chen,Lingfei Liu,Xiu Luo1,Huaiting Gu,Xin Li,Jiajia Zhang,Jianhua Gong,Ranran Song 대한신경정신의학회 2019 PSYCHIATRY INVESTIGATION Vol.16 No.5
Objective: To evaluate the association of GRIK2 and NLGN1 with autism spectrum disorder in a Chinese population. Methods: We performed spatio-temporal expression analysis of GRIK2 and NLGN1 in the developing prefrontal cortex, and examined the expression of the genes in ASD cases and healthy controls using the GSE38322 data set. Following, we performed a case-control study in a Chinese population. Results: The analysis using the publicly available expression data showed that GRIK2 and NLGN1 may have a role in the development of human brain and contribute to the risk of ASD. Later genetic analysis in the Chinese population showed that the GRIK2 rs6922753 for the T allele, TC genotype and dominant model played a significant protective role in ASD susceptibility (respectively: OR=0.840, p=0.023; OR=0.802, p=0.038; OR=0.791, p=0.020). The NLGN1 rs9855544 for the G allele and GG genotype played a significant protective role in ASD susceptibility (respectively: OR=0.844, p=0.019; OR=0.717, p=0.022). After adjusting p values, the statistical significance was lost (p>0.05). Conclusion: Our results suggested that GRIK2 rs6922753 and NLGN1 rs9855544 might not confer susceptibility to ASD in the Chinese population.
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An Adaptive Cellular Genetic Algorithm Based on Selection Strategy for Test Sheet Generation
Ankun Huang,Dongmei Li,Jiajia Hou,Tao Bi 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
Intelligent test sheet generation is a multi-objective constrained optimization problem. Genetic algorithm based on groups search strategy can provide a better solution for multi-objective optimization. Traditional genetic algorithm in test sheet generation process has many drawbacks, such as poor convergence, low fitness and high exposure times. To solve these problems, this paper proposes an adaptive cellular genetic algorithm based on selection strategy. Selection strategy can adaptively determine candidate test items set and the conceptual granularities according to the desired concept scope. Then, a new cellular population is formed by candidate test items. After evolution by the rule, genetic algorithms are executed. The experimental results show that the proposed algorithm gets rid of tests that do not meet the requirements which can reduce knowledge related errors, lower the exposure of tests, and increase the possibility of escape from local optima. In general, the algorithm proposed in this paper effectively improves the convergence speed as well as generates test papers more in line with people's demands.
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Efficient Metric Vector-Based Code Clone Detection Using Function-calling Tree
Wei Li,Dongmei Li,Chengjing Qiu,Jiajia Hou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11
Most traditional code clone detections have less accurate results because they ignore the structure of the program itself, and some of them really think about it by creating a complex syntax tree but leading to a high time complexity. Confronting such situation, this paper proposes an efficient metric vector-based code clone detection method using function-calling tree. Considering the two program code to be detected, feature vectors in all defined functions of the two different code are extracted first. Then, two function-calling trees are created according to the function-calling process and node matches each other between two trees, at the same time, the matching similarities are calculated. Finally, by using the bottom-up approach and combining similarity values of all child nodes, the detection can get the similarity of the two program code to be detected. Our experiment selects a set of typical code sample to measure and the results demonstrate that, compared the famous JPlag system, it shows better detection effect.
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Concept Similarity Measure with Hierarchy Structure and Information
Cong Dai,Dongmei Li,Hui han,Qichen Han,Jiajia Hou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.8
Calculating semantic similarity between concepts in ontology is an important issue in natural language processing and so on, so how to measure the similarity becomes a hot topic among many scholars. However, most existing methods cannot distinguish the similarity further. Confronting this problem, we propose a new semantic similarity method combining hierarchy structure of ontology and information content of two concepts based on domain ontology, which highlights the semantic information of leaves in the ontology structure. Our experiment demonstrates that, compared with other available methods, our proposal can improve the accuracy between two leaves and between leaf and non-leaf.
KCI
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