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| 1 | 基于多源数据融合的阿尔茨海默症调控网络构建显示文摘转录调控网络的重建是从分子水平研究疾病致病调控机制的重要内容,而目前大多重建方法都是基于单一的基因表达数据,缺乏考虑多基因相互作用与致病机制的联系。基于此,本研究通过整合基因表达数据、蛋白质相互作用数据及Motif数据等多源高通量生物数据构建阿尔茨海默症(Alzheimer's disease,AD)转录调控网络,它不仅重构了转录因子(transcription factor,TF)对靶基因的调控作用,还考虑了TF之间以及靶基因之间的共表达等相互关联作用。仿真结果及生物学分析显示,重建的调控网络中检测到许多功能相关的靶基因如E2F4、ATF1等及其相互作用与AD致病密切相关,并挖掘出如Toll样受体4信号通路的负调控、HTLV-1感染等与AD致病机制可能相关的通路。 | 孔薇 丁杰媛 王帅群 | 2018 | 基因组学与应用生物学2018，37，12： | 0 |
| 2 | Finding finer functions for partially characterized proteins by protein-protein interaction networks显示文摘Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins. | LI YanHui GUO Zheng MA WenCai YANG Da WANG Dong ZHANG Min ZHU ding ZHONG GuoCai LI YongJin YAO Chen WANG Jing | 2007 | Chinese Science Bulletin2007，52，24： | 0 |
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