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您的检索式：作者名="Shouchuang Shi"
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| 1 | Rice metabolic regulatory network spanning the entire life cycle显示文摘As one of the most important crops in the world,rice(Oryza sativa)is a model plant for metabolome research.Although many studies have focused on the analysis of specific tissues,the changes in metab-olite abundance across the entire life cycle have not yet been determined.In this study,combining both tar-geted and nontargeted metabolite profiling methods,a total of 825 annotated metabolites were quantified in rice samples from different tissues covering the entire life cycle.The contents of metabolites in different tissues of rice were significantly different,with various metabolites accumulating in the plumule and radicle during seed germination.Combining these data with transcriptome data obtained from the same time period,we constructed the Rice Metabolic Regulation Network.The metabolites and co-expressed genes were further divided into 12 clusters according to their accumulation patterns,with members within each cluster displaying a uniform and clear pattern of abundance across development.Using this dataset,we established a comprehensive metabolic profile of the rice life cycle and used two independent strategies to identify novel transcription factors-namely the use of known regulatory genes as bait to screen for new networks underlying lignin metabolism and the unbiased identification of new glycerophospholipid metabolism regulators on the basis of tissue specificity.This study thus demonstrates how guilt-by-association analysis of metabolome and transcriptome data spanning the entire life cycle in cereal crops provides novel resources and tools to aid in understanding the mechanisms underlying important agro-nomic traits. | Chenkun Yang Shuangqian Shen Shen Zhou Yufei Li Yuyuan Mao Junjie Zhou Yuheng Shi Longxu An Qianqian Zhou Wenju Peng Yuanyuan Lyu Xuemei Liu Wei Chen Shouchuang Wang Lianghuan Qu Xianqing Liu Alisdair R.Fernie Jie Luo | 2022 | Molecular Plant2022，15，2： | 3 |
| 2 | Hybrid Optimization-Based GRU Neural Network for Software Reliability Prediction显示文摘Aiming at the problems of low prediction accuracy and weak generalization ability of current reliability prediction models,this paper proposes a hybrid multi-layer heterogeneous particle swarm optimization algorithm(HMHPSO)that can simultaneously optimize the structure and parameters of the GRU neural network.It first introduced a multi-layer heteromass particle swarm optimization(MHPSO)algorithm,which sets the population topology as a hierarchical structure and introduces the concept of attractors,so as to improve the update formula of particle speed,and enhance the information interaction ability between particles,increase the diversity of the groups,thereby improving the optimization ability of the algorithm.Then the HMHPSO used the quantum particle swarm optimization(QPSO)algorithm to determine the structure of the GRU,that is,the number of hidden nodes.Experimental results show that the algorithm can generate GRU neural networks with high generalization performance and low architecture complexity,and has better prediction accuracy in software reliability prediction. | Maochuan Wu Junyu Lin Shouchuang Shi Long Ren Zhiwen Wang | 2020 | 国际计算机前沿大会会议论文集2020，，2： | 0 |
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