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( Chunming Wu ),( Meng Wang ),( Lang Gao ),( Weijing Song ),( Tian Tian ),( Kim-kwang Raymond Choo ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.8
The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.
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Association between STAT4 polymorphisms and risk of primary biliary cholangitis: a meta-analysis
Li Zhang,Chunming Gao,Chuanmiao Liu,Jiasheng Chen,Kuihua Xu 한국유전학회 2018 Genes & Genomics Vol.40 No.10
Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease. Several studies reported that SATA4 (signal transducer and activator of transcription 4) polymorphisms were significantly associated with PBC susceptibility. In order to derive a more comprehensive estimation of the association between STAT4 and PBC risk, this meta-analysis was conducted. Thirteen eligible studies from 8 articles with a total number of 11,310 cases and 27,844 controls were included in this meta-analysis. Pooled odds ratios (OR) and 95% confidence intervals (CI) were estimated with fixed effects model or random effects model. The results showed statistically significant association between polymorphisms of rs7574865, rs3024921, rs6752770, rs7601754 and rs10168266 in STAT4 and PBC risk under the allelic effect model (rs7574865, T vs. G, OR = 1.24, 95% CI 1.14–1.35; rs3024921, T vs. A, OR = 1.65, 95% CI 1.44–1.91; rs6752770, G vs. A, OR = 1.24, 95% CI 1.11–1.39; rs7601754, A vs. G, OR = 1.35, 95% CI 1.17–1.55; and rs10168266, T vs. C, OR = 1.31, 95% CI 1.22–1.41). Furthermore, the rs7574865 polymorphism was significantly associated with PBC risk under all genotype genetic models (dominant effect model: TT + TG vs. GG, OR = 1.43, 95% CI 1.19–1.71; recessive effect: TT vs. TG + GG, OR = 1.40, 95% CI 1.24–1.58; and co-dominant effect: TT vs. GG, OR = 1.67, 95% CI 1.37–2.02). The sensitivity analysis by omitting one study at a time showed that the results were stable. No publication bias was indicated from both Begg’s test and Egger’s weighted regression. This meta-analysis suggested that polymorphisms of rs7574865, rs3024921, rs6752770, rs7601754 and rs10168266 in STAT4 were significantly associated with the risk of PBC.
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