基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究进展

蛋白质相互作用网络中的模块化结构通常对应于蛋白质复合物或者蛋白质功能模块。基于蛋白质相互作用网络预测蛋白质复合物和功能模块不仅有助于理解生命有机体的细胞生物过程,而且可为探讨疾病的发生、发展和治疗以及合理的药物开发提供重要的基础。本文通过回顾近二十年来基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究的发展历程,按照静态蛋白质相互作用网络(SPIN)和动态蛋白质相互作用网络(DPIN)两个方向分别梳理预测算法所涉及的方法和技术,同时归纳常用的数据集并分析所面临的问题,为进一步研究提供有价值的参考。     

Novel recursive inference algorithm for discrete dynamic Bayesian networks

Dynamic Bayesian networks (DBNs) can effectively perform modeling and qualitative reasoning for many dynamic systems. However, most of its inference algorithms involve complicated graphical transformations that are hard to program and time-consuming to compute. This article proposes a new recursive inference algorithm, which is a purely numerical method derived from probability theory and the characteristics of Bayesian networks to do both on-line and off-line inferences in discrete DBNs. The most prominent advantages of this novel approach include: (1) it is an exact inference algorithm, thus its accuracy and stability can be guaranteed, (2) it avoids the complex graphical transformation so as to remarkably improve the inference speed, and (3) its concise recursive formulae facilitate programming of both forwards and backwards pass. All of these issues are verified by accurate mathematical derivation as well as a couple of application examples with comparison between the new algorithm and the two most prevailing inference approaches of discrete DBNs – the interface algorithm and the forwards–backwards algorithm.     

基于离散时间贝叶斯网络的动态故障树分析方法

提出了一种基于离散时间贝叶斯网络的动态故障树分析方法．首先给出优先与门、顺序相关门、备件门、功能相关门等动态逻辑门向离散时间贝叶斯网络的转化方法，在得到动态故障树对应的离散时间贝叶斯网络之后，再利用贝叶斯网络推理算法计算、诊断和预计顶事件概率、重要度等常规分析结果．对数字飞控计算机系统进行的分析表明，该方法能够保证较高的求解精度，其相对误差均保持在0．4%以内，而且易于扩展到多态和非确定性逻辑关系的情形．     

复杂环境下基于动态贝叶斯网络的目标识别

为了提升高动态复杂电磁环境下空战过程中对目标的识别能力,针对SBN网络模型无法满足战场的动态性要求以及对目标的经常性误识别问题,设计了一种基于变结构动态贝叶斯网络的目标类型识别模型。该模型是由静态贝叶斯网络模型演变而来,具有良好的动态表达性和滤波功能,弥补了SBN的不足,并且对空战过程中目标特征信息丢失的问题有良好的容错能力。仿真结果表明,基于动态贝叶斯网络的目标识别的识别效果,优于基于参数学习贝叶斯网络的目标识别。使用该模型后目标识别的准确性提高了5%,有效地解决目标类型识别过程中数据缺失和信息不足的问题。     

动态贝叶斯网精确推理中联合树算法的改进

基于动态贝叶斯网络处理动态不确定性问题的过程中推理是非常重要的,而推理算法的优劣决定推理的执行效率;文章在分析联合树性质的基础上提出一种较简单的3/2片联合树算法,该算法不需要限制消去顺序且只作一次扩展;讨论了算法的复杂度,并作了试验比较。     

一种基于小数据集的贝叶斯网络学习方法

 贝叶斯网络是用来表示不确定变量集合联合分布的图形模型,反映了变量间潜在的依赖关系.从完备数据集和不完备数据集上学习贝叶斯网络是研究的热点之一,要求有大数据集.针对实际应用中常常只能获得小样本数据,提出了基于Bootstrap抽样的网络结构学习的遗传算法,实验结果表明该方法在小数据集上学习贝叶斯网络具有一定的有效性.     

基于动态贝叶斯网络的交通流状态辨识方法

为准确地对交通流状态进行辨识,进而支持交通流实时诱导系统有效运行,结合速度、流量与车道占有率3种交通流参数,将贝叶斯网络用于交通流状态辨识,提出了基于动态贝叶斯网络的交通流状态辨识方法. 利用英国南安普敦市的实际数据对上述方法进行了仿真验证. 验证结果表明,利用动态贝叶斯交通流状态辨识方法可以更加准确地判别出交通流所处的运行状态,这为智能交通系统,特别是交通流实时诱导系统,提供了一定的理论支持.      

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and 'date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes--or modules--to each other, whereas party hubs function inside modules.     

Dynamic Characteristic Study of Ball Screw Feed Drive Systems Based on Multi-flexible Body Model

A new ball screw dynamic model was developed under the adequate consideration of the interaction in the screw-nut assembly （not only the mutual-coupling factors but also the self-coupling factors） . Based on this model,the multi-flexible body （MFB）dynamic model of ball screw feed drive system was then founded in order to take full account of the influencing factor of system flexibility and study the dynamic behaviors of the whole mechanical transmissions. Moreover,the MFB based state space modeling was proposed by modal state space method, which extraced the eigenmodes of more dominant modes and applied them into an MFB state space model,and realized the integrated model of servo drives and MFB mechanical transmissions more effectively and efficiently. In conclusion,the comparisons between simulations and experimental results show： the stiffness formulation of the ball screw assembly derived above is a suitable method for achieving accurate MFB models of ball screw mechanical transmission systems,this proposed MFB model is valid,and the integrated model of ball screw feed drive system is accurate and reliable. All these provide the important approaches and guidelines for dynamic characteristic study and selection of control parameters in the machine tool design period.     

基于序列剖面和可及表面积的蛋白质相互作用位点的预测

蛋白质相互作用位点的预测对于突变设计和蛋白质相互作用网络的重构都是至关重要的.由于实验确定的蛋白质复合物和蛋白质配体复合物的结构依然相当少,预测蛋白质相互作用位点的计算方法就显得十分重要.该文提出了一种以支持向量机为分类器,以邻近残基的序列剖面和可及表面积为输入数据来预测蛋白质相互作用位点的方法.计算结果显示,界面残基和非界面残基被识别的准确率为75.12%,假阳性率为28.04%.与输入数据仅有序列剖面的方法相比,界面残基和非界面残基被识别的准确率提高了4.34%,假阳性率降低了4.63%.     

一种基于动态贝叶斯模型的大型IP网络故障诊断方法

为了提高IP业务的服务质量,利用告警等症状和已有知识快速准确地定位根故障十分重要。基于贝叶斯网络的不确定推理方法是近年来广泛应用的一种故障诊断方法。目前,基于静态贝叶斯网络的故障定位只是利用当前信息进行故障诊断,无法处理时间信息;而已有基于动态贝叶斯网络的诊断算法复杂度太高,不适用于大型网络。本文针对大型IP网络,建立用于故障诊断的动态贝叶斯模型,并对基于动态贝叶斯网络的一种通用的精确算法进行改进,实验证明它能够对大型IP网络快速准确的定位故障。本文方法充分利用告警库中的历史数据和当前症状信息,对当前的系统状态进行估计,完成故障诊断。     

随机动态条件驾驶员多讫地出行路径选择研究

为解决驾驶员多讫地出行路径选择问题,综合各评价指标,提出了势力圆法,并对不同重要度讫地的势力圆分别进行了讨论研究. 结合离散动态贝叶斯网络,建立了多讫地出行的驾驶员路径选择动态决策模型. 通过具体算例对该模型进行了仿真验证,结果表明,综合势力圆法和离散动态贝叶斯网络方法在多讫地出行的路径决策中有着很好的适用性.      

一种新的人才培养模式——“动态能力集”工学结合人才培养模式研究

“动态能力集”工学结合人才培养模式强调能力是动态的、是一种集合。核心思想是依据行业需求，以就业为导向，以能力为中心，将实际工作岗位所需求的各种能力任务化、项目化，构建人才培养动态能力集。根据任务化了的动态能力集再确定相应的课程体系、动态的能力模块、动态的能力点，并采用工学交替、任务驱动的教学模式，做到真正的知行合一。     

面向对象的电网整定计算和故障仿真软件

继电保护整定计算和电网故障计算的自动化和图形化是电力系统发展的必然趋势。文章介绍了在 Visual C++下开发的、基于 OOP技术的图形化继电保护整定计算和电网故障仿真系统软件 NSW,论述了 NSW的总体结构、组成模块及采用的主要技术 ,并详细阐述了它的特点 ,最后介绍了软件技术的应用情况     

具有耦合时滞的时变复杂动态网络的函数投影同步

研究了具有时变耦合时滞的时变复杂动态网络的函数投影同步,通过设计合适的反馈控制器获得了复杂动态网络系统实现函数投影同步的法则.通过数值模拟验证了该理论的正确性和有效性。     

The response of collision speed caused by the large bus to new flexible barrier

In order to study the response of collision speed caused by the large bus to new flexible barrier, in this paper, with the large bus as the carrier, the full-scale impact tests between flexible barrier and vehicle with the impact velocities of 40 km/h and 60 km/h were carried out separately, following the procedures of the test preparation, test processing, data acquisition, etc, which were based on the test platform of the Large Structure Crash Testing Laboratory of Changsha University of Science and Technology. The important test results which contain the damage of vehicles and barrier, the moving locus of vehicle, the occupant risk index, the maximum dynamic deformation, etc, were obtained through the analysis under the different collision speeds. These provide the necessary reference basis for the further research on the structure topology optimization and improve the comprehensive constraint performance to the flexible barrier.     

CUDA＇s Mapped Memory to Support I/O Functions on GPU

The API interfaces provided by CUDA help programmers to get high performance CUDA applications in GPU, but they cannot support most I/O operations in device codes. The characteristics of CUDA＇s mapped memory are used here to create a dynamic polling service model in the host which can satisfy most I/O functions such as read/write file and ＂printf＂. The technique to implement these I/O functions has some influence on the performance of the original applications. These functions quickly respond to the users＇ I/O requirements with the ＂printf＂ performance better than CUDA＇s. An easy and effective real-time method is given for users to debug their programs using the I/O functions. These functions improve productivity of converting legacy C/C＋＋ codes to CUDA and broaden CUDA＇s functions.     

Preparation, Characterization, and Comparison of Thermal Properties of Aromatic Polyether Imides

Two aromatic polyimides were prepared by two- step way. Firstly, the poly（amic acid）s were synthesized from 1, 3 - bis（4 - aminophenoxy）benzene and two different aromatic dianhydrides. Secondly, polyimides were prepared via thermal imidization of poly（amic acid） precursors. The polyimides prepared were insoluble in common organic solvents such as tetrahydrofuran, chloroform and N, N- dimethyiformamide. The inherent viscosities of poly（amic acid）s were 1.82 dL/g and 2.67 dL/g. The high inherent viscosities were due to the strong intra- or intermolecular hydrogen interaction. The polyimides were characterized by mechanical and thermal analysis. It was found that the samples are of excellent thermal stability, higher glass-transition temperature and excellent dynamic mechanical and thermal properties.     

一种基于贝叶斯理论的软件可靠度评估方法

针对目前软件测试过程中可靠性度量方法匮乏的不足,提出了一种新的基于贝叶斯理论的软件可靠度评估方法,该方法通过分析软件模块间的影响关系构建贝叶斯网络,利用贝叶斯方法和经验数据评估贝叶斯网络中各节点的可靠度.给出了可靠度计算的详细推导过程,并通过一个具体实例解释了计算过程.该方法能重用已有的测试结果,计算速度快,简单易行.通过对每个模块提供数值可靠度估算,可以作为测试用例选择的依据,降低了测试成本,从而很好地为软件测试提供支持.     

在非时齐马氏决策过程中的动态贝叶斯网络研究

提出了用动态贝叶斯网络（DBN）对非时齐马氏决策系统进行建模的改进方法，使动态贝叶斯网络能被更广泛地应用于各种复杂的真实系统中．该方法的基本思路是，将扩展后的隐藏变量引入DBN的演化过程来建立假设条件所要求的马尔可夫模型，给出从不完整的样本数据集以及存在隐藏变量时来学习DBN结构的算法，进而用贝叶斯概率统计方法对后来的时间片的充分统计因子进行估计，并通过当前已存在的和估计的充分统计因子对基于时间变化的转移概率进行学习，以解决假设条件要求的转移概率的时不变性．原理性分析和仿真实验结果也验证了改进方法的有效性．