INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.

On the Topological Entropy of Nonautonomous Differential Equations

The purpose of this paper is to extend the concept topological entropy to nonautonomous linear systems. Next, we shall give estimation of the topological entropy for the class of bounded linear equations on Rn. Finally, we are about to investigate the invariant properties of one through the transformations such as topological conjugacy, topological equivalence and kinematically similar and then show that topological entropy of one is equal to sum of positive Lyapunov characteristic exponents.

Fractal growth kinematics abstracted from snowflakes:topological evolution

Based on the kinematic viewpoint, the concept of proportional movement is abstracted to explain the strange behaviors of fractal snowflakes. A transformation group for proportional movement is defined. Under the proportional movement transformation groups, necessary and sufficient conditions for self-similarity of multilevel structures are presented. The characteristic topology of snowflake-like fractal patterns, identical to the topology of ternary-segment fractal line, is proved. Moreover, the topological evolution of N-segment line is explored. The concepts of limit growth and infinite growth are clarified,and the corresponding growth conditions are derived. The topological invariant properties of N-segment line are exposed. In addition, the proposition that the topological evolution of the N-segment line is mainly controlled by the topological invariant N is verified.

基于离散时间量子漫步的链路预测算法

量子漫步算法能模拟游走粒子在图上的量子相干演化,粒子的运动状态由量子态的相干叠加而成.与经典随机游走算法相比,量子漫步算法具有寻找目标节点时间少和源节点扩散至其他节点时间少的优点.提出一种基于离散时间量子漫步的链路预测(link predictionbased on discrete time quantum walk,简称LP-DTQW)算法.研究结果表明:相对于其他7种算法,LP-DTQW算法有更高的预测精度;LP-DTQW算法的时间复杂度远低于经典RWR(random walk with restart)链路预测算法的时间复杂度.因此,LP-DTQW算法具有更强的预测性能.

融合用户关系表示和信息传播拓扑特征的信息传播预测

信息传播预测旨在分析信息传播网络或社交媒体中信息扩散的规律,以理解和预测信息的传播过程。现有工作主要关注用户群体关系中的社交关系和动态影响关系,忽略了群体关系中用户间相似性关系和个体关系中内在因素对用户转发信息的影响。为此,提出了融合用户关系表示和信息传播拓扑特征的信息传播预测模型,从群体关系和个体关系两个层面分析用户受信息影响的可能性。在群体关系层面,构建用户共现次数图来学习用户相似性关系表示,随后融合了用户关系表示和信息传播拓扑特征来更全面地捕捉群体关系;在个体关系层面,融合了用户个体特征表示和影响因素向量,以捕捉不同的内在因素对刺激用户转发信息h的影响。实验结果表明,该模型在两个公开数据集上性能均有提升,在Memetracker数据集上MAP@k和its@k评价指标分别平均提升了6.54%和2.75%。

基于多特征符号聚合近似和层次聚类的户变关系识别方法

针对低压配电台区拓扑档案中可能存在的户变关系异常问题,文中提出了一种基于多特征符号聚合近似(MF-SAX)和层次聚类的户变关系识别方法。首先,运用符号聚合近似表达方法将用户电压时间序列转化为字符串序列,并引入电压波动系数和电压变化趋势两个附加参数对其特征表达进行强化。然后,基于编辑距离生成用户电压曲线相似性矩阵,并结合层次聚类算法实现户变关系的识别。最后,实际算例结果表明,提出的方法相比于现有方法准确率更高,误报更少,能直接应对数据缺失的情况,且具有更高的效率。

基于节点交互度的社会网络链路预测

链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.

基于结构相似性的并联机构拓扑与尺寸同步设计方法

随着并联机构从单一尺寸设计和拓扑设计向两者融合方向不断发展,基于拓扑叠加-再惩罚和全变量正交设计-再拓扑的同步设计方法得到广泛应用,但两者存在设计效率低等不足。为解决上述问题,本文提出了一种基于结构相似性的并联机构拓扑与尺寸同步设计方法,该方法拓扑优化时以相同质量保留此条件下尺寸参数小范围变化对拓扑结构微弱影响为切入点,通过拓扑结构参数化缩放和刚度-质量元模型构建,形成并联机构尺寸-拓扑同步高效设计流程。以典型■RS并联机构为例开展算例验证与对比分析,结果表明,相同质量条件下,所提设计方法比尺寸设计方法得到的各向刚度更高,即拓扑优化使RS连杆的材料分布更加合理。

基于聚类网架拓扑的多能互补配电网分区规划方法

在多能互补配电网规划过程中,由于网架拓扑聚类方法的差异,导致分区规划后供电平均综合线损率依旧较高。因此,提出聚类网架拓扑下的多能互补配电网分区规划方法。针对配电网内馈线运行特点,将网架拓扑结构以抽象化形式表征出来,引入SimRank++算法计算网架拓扑的相似度,并基于相似度距离设计网架拓扑聚类方法,基于聚类合并原理,构建多能互补配电网分区模型,结合配电网运行工况,完成配电网子区主次网架规划。算例分析结果表明,应用所提方法进行配电网分区规划,与规划前相比使得平均综合线损率降低了10.6%。

计及拓扑相似性的含分散式风电配网双层优化重构方法

高比例风电接入背景下,配网优化重构是缓解弃风、失负荷问题的一种有效手段。多对象优化重构作为非凸非线性优化问题,同时含大量离散变量求解难度较大,为此建立了基于拓扑相似性的含高比例分散式风电配网双层交互式优化重构模型。模型设计了支路复介数相似度评价指标,来衡量所有辐射状拓扑的相似度,再将相似网络进行聚类,形成了重构层的树状解空间。重构层以分段/联络开关状态为决策变量优化网络拓扑结构,并传输至调度层;调度层以分散式风电出力、储能运行状态、无功补偿装置功率及需求响应量为决策变量,进行配电网有功无功协同优化调度,以确定各对象最优运行状态并反馈到重构层,两层不断交互迭代得出最终策略。为适应该模型提出了一种剪枝和声搜索算法,其中和声库从树枝切换到枝内的过程,提高了重构层网络重构的求解效率。采用IEEE 33节点系统仿真测试,验证了所提方法在提升系统电压安全性、降低系统运行成本方面的优越性。

光影与算法:人工智能类摄影图像的拓扑像似研究

本文在人工智能与摄影相交融的背景下,借助符号美学理论,深入探讨人工智能所生成的类摄影图像与传统摄影之间的内在关联。通过“拓扑像似”这一概念,将人工智能生成的类摄影图像细分为“文字转换的邻域拓扑”“历史解构的集群拓扑”以及“影像本体的时间拓扑”三个层面,分别从文本阐释多样性、艺术风格转变以及时间意义重构等角度出发,剖析人工智能类摄影图像在视觉表现和文化意义上的独特之处,旨在为探究这一图像类型提供新的理论视角和研究路径,丰富和发展当代艺术与科技交叉领域的研究。

基于拓扑相似度分析和密度聚类的含新能源配网分群

针对新能源接入后配网拓扑呈现动态复杂变化,现有聚类算法存在一定局限性而导致含新能源配网分群效果不佳的问题,提出了一种基于拓扑相似度分析和密度聚类的含新能源配网分群方法。首先,该方法基于拓扑图信息构造相应的邻接矩阵以表征配网拓扑结构;其次,利用奇异值分解对邻接矩阵进行分解并得到相应的奇异值序列;最后,利用密度聚类算法对所得奇异值序列进行聚类,并得到最终的含新能源配网分群结果。算例测试结果表明,相比于传统分群方法,所提方法所得集群内配网拓扑相似度更高,集群间配网拓扑差异性更为显著,分群效果更好。

Topological Similarity-Based Multi-Target Correlation Localization for Aerial-Ground Systems

In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.

Erratum to:Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

The original version of this article unfortunately contained a mistake in the name of the last author.The corrected name is Yi-Ping Phoebe Chen.

Similar Vertices and Isomorphism Detection for Planar Kinematic Chains Based on Ameliorated Multi-Order Adjacent Vertex Assignment Sequence

Isomorphism detection is fundamental to the synthesis and innovative design of kinematic chains(KCs).The detection can be performed accurately by using the similarity of KCs.However,there are very few works on isomorphism detection based on the properties of similar vertices.In this paper,an ameliorated multi-order adjacent vertex assignment sequence(AMAVS)method is proposed to seek out similar vertices and identify the isomorphism of the planar KCs.First,the specific definition of AMAVS is described.Through the calculation of the AMAVS,the adjacent vertex value sequence reflecting the uniqueness of the topology features is established.Based on the value sequence,all possible similar vertices,corresponding relations,and isomorphism discrimination can be realized.By checking the topological graph of KCs with a different number of links,the effectiveness and efficiency of the proposed method are verified.Finally,the method is employed to implement the similar vertices and isomorphism detection of all the 9-link 2-D0F(degree of freedom)planar KCs.

基于GIS和电压波形相关性分析的低压配电网拓扑识别方法

针对低压配电网拓扑结构人工校验成本高、效率低等问题,提出了一种基于GIS和电压波形相关性分析的低压配电网拓扑识别方法。首先根据GIS平台里台区配变和用户的地理信息,结合户变关系空间约束,筛选出重叠区域和非重叠区域,初步确定非重叠区域的户变关系。其次分析重叠区域用户和相邻台区配变的电压序列相关性,分别采用欧氏距离、DDTW、皮尔逊相关系数3种指标进行评估,以确定重叠区域的户变关系。最后持续动态监控重叠区域的电压相关性趋势,当用户和台区配变的电压相关性发生剧烈变化时,重新计算用户和相邻台区配变的电压相关性,判断用户所属台区,向运维人员推送告警信息以待人工校核确认。算例验证了本文方法的有效性和可行性。

不同拓扑结构分子筛催化剂上正十二烷异构化反应性能比较研究

采用硅铝比相近的Y分子筛、ZSM-5分子筛和β分子筛分别负载贵金属约0.5%Pt制备催化剂,通过XRD、XRF、TEM、N_(2)物理吸附-脱附、NH_(3)-TPD、Py-FTIR、^(27)Al核磁等手段研究了不同拓扑结构分子筛晶体结构、元素组成、孔结构、酸性以及Al分布的差异,进而考察了拓扑结构对催化剂催化正十二烷异构化反应性能的影响。结果表明,当硅铝比接近时,ZSM-5分子筛总酸量及骨架铝比例最高、平均孔径最小,Y分子筛总酸量及骨架铝比例最低、平均孔径最大,而β分子筛的总酸量、骨架铝比例以及平均孔径介于两者之间,说明不同拓扑结构分子筛硅铝原子组合方式不同,不仅影响孔道结构和孔口尺寸,而且会因为铝所处位置的不同而影响酸性;在正十二烷异构化反应中,Pt/ZSM-5催化剂活性最高且主要发生的是外表面无选择性反应,Pt/Y催化剂活性最低且异构化反应主要在孔道内,而Pt/β催化剂活性介于两者之间,异构化反应发生于孔口处以锁匙反应为主。

基于节点相似性的并行启发式网络拓扑结构发现算法

针对网络社区发现算法的低精确度与高复杂度问题,在节点相似性的基础上提出了一种适用于复杂网络的社区发现算法。利用邻接矩阵和经典决策规则,制定具有较高可靠性和准确性的相似性判定准则,实现网络拓扑中相邻节点之间关系的量化与计算。通过引入具有较少冲突的哈希表,提出具有并行运算结构的启发式社区发现算法,完成对复杂网络拓扑结构的高质量检测。仿真测试结果表明,与经典的Jaccard准则发现算法相比,基于节点相似性的发现算法具有更高的发现精确度及时间效率。

基于电压聚类排序的中压配电网拓扑在线辨识方法

网络拓扑辨识作为配电网运行和管理的基础,随着城市配电网结构的日趋复杂以及运行安全性和可靠性需求的日渐提高,配电网拓扑辨识面临着严峻挑战。为此,本文提出一种基于电压聚类排序的中压配电网拓扑在线辨识方法。首先梳理了配电网的典型拓扑结构,分析了配电网运行时拓扑的多样性和拓扑辨识的重要性;其次,分析了中压配电网电压的分布特征和相似性,通过挖掘节点电压相似性中蕴含的拓扑信息,利用均值漂移算法辨识分支、利用平均电压法辨识分支结构,实现对配电网拓扑结构的准确辨识;最后,采用某东部城市的负荷曲线,通过算例验证了所提拓扑辨识方法的有效性及优越性。

混合线性非线性网络的多源miRNA-疾病关联预测方法

现有miRNA-疾病关联研究大多采用miRNA功能和疾病语义相似性作为输入,未考虑miRNA序列和疾病功能等相似性信息,并且在特征提取过程中忽略了线性与非线性特征间的信息互补,影响特征质量。本文提出一种miRNA-疾病关联预测模型GCNMSF,它基于miRNA和疾病多源相似性信息,融合嵌入卷积注意块的图卷积网络学习的非线性特征和非负矩阵分解方法学习的线性特征,实现信息互补,以预测miRNA-疾病关联。实验结果表明,GCNMSF模型优于现有的miRNA-疾病关联预测方法,可以有效预测miRNA-疾病关联。