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您的检索式：作者名="Yuanchang LIN"
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| 1 | Identification of WSB1 gene as an important regulator in the development of zebrafish embryo during midblastula transition显示文摘为了揭开,新奇基因潜在地在胚胎包含了开发,特别在中间的胞胚转变(MBT ) 在斑马鱼的发展中的胚胎的阶段, Affymetrix 斑马鱼 GeneChip 微数组分析在 14,900 个基因抄本的表达式上被执行。分析的结果证明 360 基因清楚地是起来调整的, 119 基因是显著地下面调整的。许多这些基因涉及转录因子活动, nucleic 绑定,和房间生长。现在的学习证明在抄本丰富的重要变化发生在 MBT 阶段期间。这 479 基因中的八个的表达式被反向的抄写聚合酶链反应分析识别,证实微数组结果。WSB1 基因,发现由微数组和反向的抄写聚合酶链反应分析下面调整,为进一步的学习被选择。WSB1 基因的顺序分析证明它与 75% 身份编码蛋白质到相应活跃人的 orthologs。另外, WSB1 基因表达在 4 h 柱子授精在 2 h 柱子授精并且在底层在高水平被检测,与薄片结果一致。WSB1 基因的 Overexpression 能在胚胎导致畸形的形成,由激活荧光的房间排序决定了。现在的学习不含糊地证明 WSB1 表示的出现在在斑马鱼胚胎的开发的 MBT 阶段期间是一个重要事件。 | Wenjian Lv Yunbin Zhang Zhili Wu Lin Chu S. S. Koide Yuguang Chen Yuanchang Yan Yiping Li | 2008 | Acta Biochimica et Biophysica Sinica2008，40，6： | 1 |
| 2 | Automated GUI widgets classification显示文摘1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these tasks.Researchers in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific type.The authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI widgets.Chen et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model. | Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING | 2023 | Frontiers of Computer Science2023，17，1： | 0 |
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