乳腺癌易感基因蛋白质网络的构建与研究

本文从复杂网络理论出发,在分析原有乳腺癌易感基因数据的基础上,综合统计分析易感基因彼此之间的关联与乳腺癌疾病之间的关系,并以此构建乳腺癌致病基因蛋白质网络．通过计算和研究网络度,聚类系数等指标发现,此网络具有高度聚集性,即少数核心节点控制着整个网络结构的稳定性．这将为进一步研究和发现乳腺癌致病基因提供新的理论依据和方法．     

带耦合时序网络、重要性节点识别及投资组合研究—–以股票市场为例

时序网络可以更好地描述复杂网络中节点拓扑结构的动态演变.考虑到节点在不同时间层的相互影响及多层网络中的层间时序关联耦合关系,本文提出了一种基于向量自回归(VAR)模型的带耦合时序网络,研究网络的构建过程及性质,并将其应用于纳斯达克100、标普500、深证100和上证180四个股票市场的实证分析.结果表明:与已有模型(比如文献[15]及[16])相比,本文提出的带耦合时序网络模型无论在重要性节点识别的分辨率,还是投资组合的内样本及外样本表现上,都具有明显的优势.同时本文还基于重要性节点序列探讨了“外围”股票的确定方法.这些研究可进一步丰富时序网络理论,为金融市场研究提供新的技术工具.     

耦合动力系统的Conley指标

从Hessian矩阵入手,研究了几种有限网络上动力系统的两种孤立不变集的Conley指标.对于网络节点仅具有单个状态参量时,分析了在线性耦合关系和非线性耦合关系下的全连接网络(全耦合网络)、最近邻网络和任意连接网络上流的孤立不动点的Conley指标与单个节点状态流的孤立不动点的Conley指标的关系;对于节点具有双状态参量和孤立节点不变集为极限环时,给出了在一定耦合条件下,文中所定义的映射F(s)的C0nley指标与节点状态积流的Conley指标的关系.     

基于贝叶斯网络的基因调控研究

为研究基因间的网络调控关系,通过贝叶斯网络方法将概率论知识与图论结合,有效构造了基因间的贝叶斯网络模型并进行了推理.针对一组白血病基因表达数据,首先进行数据标准化、离散化等预处理;其次使用决策树ID3算法求出基因间节点的顺序,并使用K2算法进行贝叶斯网络的结构学习,找出各基因间的网络拓扑结构;再次通过极大似然估计进行参数学习,求出网络中父节点与子节点间的概率依赖关系;最后对构建的贝叶斯网络模型进行了有效性验证,检验数据分析表明,贝叶斯网络对基因间调控关系的预测分析有较高精度.     

显著性分析(SAM)方法在乳腺癌基因芯片数据分析中的应用

对乳腺癌基因芯片试验结果进行数据分析,寻找在正常组织与癌组织中呈现差异表达的基因.运用微阵列芯片显著性分析(SAM)方法进行差异表达基因的筛选,并使用permutation算法计算错误发现率(FDR).一些呈现差异表达的基因被筛选出来,其中一部分基因已被数篇文献报道过,认为它与乳腺癌发病相关.SAM方法比较适用于对基因芯片实验的结果进行相关基因的初步筛选,筛选出的基因可用于为进一步的研究提供候选基因.     

偶图中过给定独立边集的圈

这里考虑的一切图均为简单的,以V(G),E(G)分别表示图G的节点集和边集。设H是G的子图,x∈V(H),用d_H(x)表示H中与节点x相邻节点的个数。如果e=(x,y)∈E(H),x,y是e的端点,则让d_H(e)=d_H(x)+d_H(y)。设A、B是V(G)的两个节点不交的子集,用E(A:B)表示G中一端在A中另一端在B中边的个数。设M是G     

可靠性数学中的若干问题和进展

本文将综述可靠性数学理论中的一些重要问题和进展,其中包括网络可靠度计算、多状态coherent系统理论以及可修系统理论.由于篇幅限制,文中论题并不概括可靠性数学理论的全貌,文献也是基本的.一、网络可靠度网络系统可靠度的计算,特别是对大型复杂系统寻找有效的算法,以及各类算法优劣的比较,一直是理论和实用上都感兴趣的问题.1.基本问题给定一个网络(图)G,其中节点(顶点)集合V={v_i,…,v_n},弧(边)的集合E={e_l,…,e_b}。假定节点与弧只有正常和失效两种状态,并进一步设失效之间是互相独立的.(a)有源问题记S、t为两个特定的节点,称作“发点”和“收点”,求     

自由树的端点数问题(英文)

本文研究具有 n 个节点的、所有结构不同的自由树的端点总数,得到了关于这些数的计算公式,给出了其生成函数与 n 节点有向树个数的生成函数和 n 节点有向数总叶数生成函数之间的一个简明关系.主要结果是:定理 设 E_n为具有 n 个节点的、所有结构不同的自由树的端点总数,则 E_n 的生成函数E(z)=sum from n=1 to ∞ E_n~z~n 满足E(z)=L(z)+zA(z)-A(z)L(z)+L(z~2),其中 A(z)和 L(z)分别是 n 节点有向树的个数 a_n 和总叶数 L_n 的生成函数.此外,E_n=(?)计算结果如正文中的数表所示.     

弧k/n(G)与节点k/n(G)网络模型可靠性估计的对偶变量蒙特罗洛方法

针对弧k/n(G)网络无法刻画网络节点对上游节点输入需求的问题,提出了节点k/n(G)网络模型.在节点k/n(G)网络中,通过设定网络节点工作条件为接收工作输入点集中n个节点里的至少k个输入,节点k/n(G)性质被进一步延伸至对上游任意节点.为采用蒙特卡洛方法对弧与节点k/n(G)网络的可靠性进行估计,分别对两类k/n(G)网络设计了基于随机邻接矩阵的网络连通性算法.并结合k/n(G)网络结构函数的单调性,将对偶变量方差缩减技术应用于两类k/n(G)网络的蒙特卡洛方法,仿真实验表明:所设计的仿真方案能够有效地对两类k/n(G)网络的可靠性进行估计,对偶变量方法提高了蒙特卡洛方法的计算精度并减少了计算时间.     

基于综合效益的城市地下物流网络设计与优化方法

针对城市地下物流系统(Underground Logistics System,ULS)的特征,对一系列ULS网络节点选址与优化问题进行了建模分析.1)从解决城市交通拥堵的角度出发,探讨了物流地上地下分配的三种方案,并建立了地下货运OD评价模型.2)综合权衡货运量与货源距离,基于改进的模糊C均值聚类确定ULS—级节点的选址和辐射范围.3)对每个一级区域构建了ULS二级节点选址优化模型,通过人工免疫算法搜索最少覆盖节点群及节点的最优归属.4)建立多目标ULS网络规划模型,结合Prim算法与Dijkstra算法实现货物地下运输路径的最优选择,并采用栅格覆盖的思路在节点服务范围内对ULS网络进行费用优化.5)提出ULS网络效能评估指标,设置中心节点以提高系统运输效率和抗风险能力.     

基于提升小波的基因芯片数据的分类预测

针对肿瘤的早期诊断,提出了一种基于提升小波变换的特征提取的方法,对肿瘤数据样本进行分析鉴别.该方法利用提升小波变换对190例肝癌（包括对照）和107例肺癌（包括对照）基因表达谱芯片数据进行处理后,提取信号的低频信息,经支持向量机训练学习,构造分类器模型,用于癌和非癌样本的区分甄别.实验结果表明,经提升小波变换提取的特征基因,送入分类器中能得到较高的分类率,且在支持向量机中选取线性核函数或径向基函数都能达到较好的分类效果.通过随机选取的20例基因表达谱芯片样本,对所建立的模型进行了测试,获得了很好的效果,因此,本文提出的方法对肿瘤的诊断有一定的应用意义.     

Survival-Time Classification of Breast Cancer Patients

The identification of breast cancer patients for whom chemotherapy could prolong survival time is treated here as a data mining problem. This identification is achieved by clustering 253 breast cancer patients into three prognostic groups: Good, Poor and Intermediate. Each of the three groups has a significantly distinct Kaplan-Meier survival curve. Of particular significance is the Intermediate group, because patients with chemotherapy in this group do better than those without chemotherapy in the same group. This is the reverse case to that of the overall population of 253 patients for which patients undergoing chemotherapy have worse survival than those who do not. We also prescribe a procedure that utilizes three nonlinear smooth support vector machines (SSVMs) for classifying breast cancer patients into the three above prognostic groups. These results suggest that the patients in the Good group should not receive chemotherapy while those in the Intermediate group should receive chemotherapy based on our survival curve analysis. To our knowledge this is the first instance of a classifiable group of breast cancer patients for which chemotherapy can possibly enhance survival.     

The shape of the hazard function for cancer incidence

A population-based cohort consisting of 126,141 men and 122,208 women born between 1874 and 1931 and at risk for breast or colorectal cancer after 1965 was identified by linking the Utah Population Data Base and the Utah Cancer Registry. The hazard function for cancer incidence is estimated from left truncated and right censored data based on the conditional likelihood. Four estimation procedures based on the conditional likelihood are used to estimate the age-specific hazard function from the data; these were the life-table method, a kernel method based on the Nelson Aalen estimator, a spline estimate, and a proportional hazards estimate based on splines with birth year as sole covariate.The results are consistent with an increasing hazard for both breast and colorectal cancer through age 85 or 90. After age 85 or 90, the hazard function for female breast and colorectal cancer may reach a plateua or decrease, although the hazard function for male colorectal cancer appears to continue to rise through age 105. The hazard function for both breast and colorectal cancer appears to be higher for more recent birth cohorts, with a more pronounced birth-cohort effect for breast cancer than for colorectal cancer. The age specific for colorectal cancer appears to be higher for men than for women. The shape of the hazard function for both breast and colorectal cancer appear to be consistent with a two-stage model for spontaneous carcinogenesis in which the initiation rate is constant or increasing. Inheritance of initiated cells appears to play a minor role.     

Stability analysis of mathematical models for nonlinear growth kinetics of breast cancer stem cells

Cancer stem cells are responsible for tumor survival and resurgence and are thus essential in developing novel therapeutic strategies against cancer. Mathematical models can help understand cancer stem and differentiated cell interaction in tumor growth, thus having the potential to help in designing experiments to develop novel therapeutic strategies against cancer. In this paper, by using theory of functional and ordinary differential equations, we study the existence and stability of nonlinear growth kinetics of breast cancer stem cells. First, we provide a sufficient condition for the existence and uniqueness of the solution for nonlinear growth kinetics of breast cancer stem cells. Then we study the uniform asymptotic stability of the zero solution. By using linearization techniques, we also provide a criteria for uniform asymptotic stability of a nontrivial steady‐state solution with and without time delays. We present a theorem from complex analysis that gives certain conditions that allow for this criteria to be satisfied. Next, we apply these theorems to a special case of the system of functional differential equations that has been used to model nonlinear growth kinetics of breast cancer stem cells. The theoretical results are further justified by numerical testing examples. Consistent with the theories, our numerical examples show that the time delays can disrupt the stability. All the results can be easily extended to study more general cell lineage models. Copyright © 2017 John Wiley & Sons, Ltd.     

A model of breast carcinogenesis and recurrence after radiotherapy

Breast conserving surgery and adjuvant radiotherapy are the standard treatment of early stage breast cancer. We present simulations of a mathematical model of breast carcinogenesis and identify fields of mutated cells in the breast (formed during puberty or pregnancy) as a potential source of local recurrence after treatment with radiotherapy. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

稀疏表示及其在人脸识别中的应用

稀疏表示是近年来新兴的一种数据表示方法,是对人类大脑皮层编码机制的模拟。稀疏表示以其良好的鲁棒性、抗干扰能力、可解释性和判别性等优势,广泛应用于模式识别领域。基于稀疏表示的分类器在人脸识别领域取得了令人惊喜的成就,它将训练样本看成字典,寻求测试样本在字典下的最稀疏的表示,即用尽可能少的训练样本的线性组合来重构测试样本。但是经典的基于稀疏表示的分类器没有考虑训练样本的类别信息,以致被选中的训练样本来自许多类,不利于分类,因此基于组稀疏的分类器被提出。组稀疏方法考虑了训练样本的类别相似性,其目的是用尽可能少类别的训练样本来表示测试样本,然而这类方法的缺点是同类的训练样本或者同时被选中或者同时被丢弃。在实际中,人脸受到光照、表情、姿势甚至遮挡等因素的影响,样本之间关系比较复杂,因此最后介绍局部加权组结构稀疏表示方法。该方法尽量用来自于与测试样本相似的类的训练样本和来自测试样本邻域的训练样本来表示测试样本,以减轻不相关类的干扰,并使得表示更稀疏和更具判别性。     

Invariants of Algebras and Finite Groups

Group theoretical constructions usually terminate in the problem to decide whether two groups are isomorphic. In the case of arbitrary finite groups the calculation of ordinary group characters is not sufficient to decide about it. R. Brauer posed the problem, to find suitable additional group invariants. Applying the theory of norm-type forms of associative algebras, specialized to group algebras, we found, that the 1-, 2-, and 3-characters in the nonmodular case (with some restrictions on the characteristic), especially over the field of complex numbers, are necessary and sufficient for the finite groups. This sharpens a recent result of E. Formanek and D. Sibley on group determinants. Detailed proofs will be given elsewhere. Here we give an overview on recent related results and add a remark concerning calculations in the modular group case.     

GLRT和LS_SVM应用于基因表达数据分类

为快速、准确地对基因芯片表达数据进行分类,提出了一种新型的基因芯片表达数据分类模型.该模型首先使用广义似然比检验(GLRT)有效鉴别出表达有显著性差异的基因.然后,将这些表达有显著性差异的基因用于最小二乘支持向量机(LS_SVM)的训练,从而建立了基于GLRT+LS_SVM的基因芯片表达数据分类模型.该模型在处理数据量大、维数高、样本量小、非线性等特点的基因芯片数据时有很大优势,可以广泛用于处理基因芯片数据.     

Isomorphisms of Kac-Moody groups

We give the solution of the isomorphism problem for Kac-Moody groups over algebraically closed fields of any characteristic. In particular, we prove a conjecture of Kac and Peterson and compute the automorphism group of a Kac-Moody group over an algebraically closed field of characteristic zero. Mathematics Subject Classification (2000) 17B40, 20E36, 20E42, 20G15, 22E65, 51E24