基于DNA微阵列数据分析的分级Bayes模型

如何分离出少量区别不同组织类型的特异性基因是DNA微阵列数据分析中的主要问题,特别是构建恰当的统计模型来刻画这些不同组织类型的DNA表达形式尤为重要.为此,基于基因DNA微阵列数据的特点,我们假定对数变换后的微阵列数据服从混合正态分布.我们采用分级Bayesian先验刻画不同基因的相关性,利用分级Bayesian方法构建模型,给出了刻画不同组织基因表达的差异的一个标准,用MCMC迭代计算该标准.模拟计算表明我们的模型具有较好的识别能力.     

Vasiek利率模型下的亚式期权的定价问题和数值分析

本文研究了随机利率满足Vasiccek模型时带有浮动的敲定价格的欧式看涨亚式期权的定价问题.通过对所涉及的退化的抛物型方程的Cauchy问题进行变量代换,我们把状态空间的维数降低了一维.为克服其中的奇异性问题,本文对方程进行了分解,第一部分的方程虽然保持奇性,但是其解具有一个精确表达式;而残差部分满足系数和初始条件都充分光滑的Cauchy问题,我们运用一般的差分方法对该部分进行了有效的数值计算.     

基于动态列生成算法的飞机排班问题研究

飞机排班是航空运输生产计划的重要环节,对航空公司的正常运营和整体效益有着决定性影响;飞机排班通常构建为大规模整数规划问题,是航空运筹学研究的重要课题,构建的模型属于严重退化的NP-Hard问题.在考虑对多种机型的飞机进行排班时,大大增加了问题的复杂性.针对航空公司实际情况,建立多种机型的飞机排班模型;为实现模型的有效求解,提出了基于约束编程的动态列生成算法;即用约束编程快速求解航班连线(航班串)并计算航班串简约成本,动态选择列集并与限制主问题进行迭代.最后,利用国内某航空公司干线航班网络实际数据验证模型和算法的有效性.     

2010年研究生数学建模竞赛A题综述

第七届全国研究生数学建模竞赛A题是生物信息学中的一个急需解决的问题.虽然有关问题的研究已经经历了十多年,但由于问题的复杂性,人们的认识还很局限,基本的结论大多还以定性的为主,定量的探讨正方兴未艾.对参赛队员来讲解决该问题是一个极大的挑战.研究生们在讨论该问题时,大多直接进行分类.然而对于一个小样本的学习问题,显然这样做是行不通的.所以问题的关键是从数学和生物学角度减少用于分类的特征数目.同时,对于获取的基因标签,需要从临床上或生物学角度找到验证.该问题的求解过程引导研究生们从数学建模走向解决实际问题.     

Vasiek利率模型下的亚式期权的定价问题和数值分析

本文研究了随机利率满足Vasiek模型时带有浮动的敲定价格的欧式看涨亚式期权的定价问题.通过对所涉及的退化的抛物型方程的Cauchy问题进行变量代换,我们把状态空间的维数降低了一维.为克服其中的奇异性问题,本文对方程进行了分解,第一部分的方程虽然保持奇性,但是其解具有一个精确表达式;而残差部分满足系数和初始条件都充分光滑的Cauchy问题,我们运用一般的差分方法对该部分进行了有效的数值计算。     

急性髓细胞白血病基因筛选模型的贝叶斯分析

基因表达数据蕴含着大量的生物信息,在生物基因信息研究中,筛选表达水平发生显著变化的差异基因是认识疾病形成机理和辅助靶点药物研究的关键问题.根据急性髓细胞白血病(AML)的基因表达数据,构造基因均值差序列,建立贝叶斯分层混合模型,并为模型的参数赋予具有基因生物特征的先验信息.采用马尔可夫链蒙特卡洛(MCMC)算法对模型参数进行估计,并筛选出急性髓细胞白血病差异表达基因.在实际数据分析中,从美国生物信息中心(NCBI)的高通量基因表达数据库中获取急性髓细胞白血病基因数据集,从经过非特异滤波预处理的14688个急性髓细胞白血病基因中筛选出711个差异表达基因,差异表达基因数仅占急性髓细胞白血病基因总数的4.84%,这一结果与基因差异表达的生物学原理相吻合.     

复合二项过程下的负风险模型

本文研究了总索赔服从复合二项过程的负风险模型.通过鞅方法推导出了该模型破产概率的Lundberg不等式和破产概率的精确表达式.     

基于基因表达谱的肿瘤识别与分类特征基因提取研究

针对基因表达谱信息基因提取的问题,使用Wilcoxon秩和检验方法进行"无关基因"的剔除,基于高低水平基因表达的特点,建立了关于高/低表达水平的双线性回归模型,基于残差分析提取了19个特征基因.使用启发式宽度优先搜索算法搜索最优基因子集,确定结肠癌的基因"标签",运用支持向量机对分类效果进行检验,分类效果良好.     

约化模型中含有交易对手信用风险的可转换债券的定价

本文在约化模型中研究了含有交易对手信用风险的可转换债券的定价问题.我们假设市场中可转换债券的违约强度过程和无风险利率过程均满足Vasicek模型,通过引入测度变换的方法导出了该模型中可转换债券的定价表达式.此外,我们通过数值分析展示了模型的参数变化对可转换债券价值的影响.     

一类相依风险模型的破产问题

本文研究了一类索赔过程与索赔额大小相关的风险模型.利用无穷小方法,得到了该相依模型的折扣惩罚函数的期望满足的方程.及其拉普拉斯变换的表达式.并且给出指数索赔时的具体运用.     

An automotive bulk recycling planning model

The automotive recycling infrastructure successfully recovers 75% of the material weight in end-of-life vehicles primarily through ferrous metal separation. However, this industry faces significant challenges as automotive manufacturers increase the use of nonferrous and nonmetallic materials. As the nonferrous content in end-of-life vehicles rises, automotive shredders need to evaluate to what extent to separate nonferrous metals. We propose a recycling planning model for automotive shredders to make short-term tactical decisions regarding to what extent to process and to reprocess materials through multiple passes. In addition, the mixed integer programming model determines whether to combine materials for shipment. In a case study for automotive shredding decisions for the current composition and more polymer-intensive end-of-life vehicles, we use our model to show the sensitivity of the decision to reprocess light nonferrous metal to low and high metal prices. Contrary to observations in practice to mix light and heavy nonferrous metals for shipment, we show multiple scenarios where the model chooses to reprocess and ship separated light and heavy nonferrous metals.     

城市表层土壤重金属污染分析模型

本文先利用Matlab做出各种重金属元素浓度的空间分布图,初步得到土壤重金属污染的状况.接着用内梅罗污染指数法定量的确定土壤重金属污染最严重的地区,并用主成分分析法进行了验证.最后利用灰色-灾变与回归预测的组合模型解决了地质环境的演变问题.     

Safe density ratio modeling

An important problem in logistic regression modeling is the existence of the maximum likelihood estimators. In particular, when the sample size is small, the maximum likelihood estimator of the regression parameters does not exist if the data are completely, or quasicompletely separated. Recognizing that this phenomenon has a serious impact on the fitting of the density ratio model–which is a semiparametric model whose profile empirical log-likelihood has the logistic form because of the equivalence between prospective and retrospective sampling–we suggest a linear programming methodology for examining whether the maximum likelihood estimators of the finite dimensional parameter vector of the model exist. It is shown that the methodology can be effectively utilized in the analysis of case–control gene expression data by identifying cases where the density ratio model cannot be applied. It is demonstrated that naive application of the density ratio model yields erroneous conclusions.     

土壤重金属复合污染整体模型研究

在重金属迁移建模研究成果的基础上,考虑土壤对重金属的吸附特性,推导出土壤重金属复合污染物迁移转化整体模型.为求解该模型,分别引进Langmuir等温竞争吸附模型与土壤水分迁移模型.最后,以重金属Zn和Cd为例,数值模拟其在土壤中的垂向迁移过程,并利用MATLAB画出相关图形.     

Can matching improve the performance of boosting for identifying important genes in observational studies?

When two groups of individuals are to be compared with respect to gene expression there will often be some potentially confounding variables that differ between the groups. Matching is an established approach for obtaining comparable groups and enabling subsequent univariate tests for each gene. Alternatively, the confounders might be incorporated directly into a multivariable regression model for adjustment. In contrast to univariate tests, such models can consider all genes simultaneously. Aiming to combine the advantages of both approaches, matching and multivariable modeling, we consider a matching-based boosting procedure for fitting risk prediction models in two-group settings. This possibly allows to identify and automatically remove problematic observations that might negatively affect the regression model. Therefore, we compare the ability to identify important covariates for this combination of matching and boosting with only boosting for different covariate correlation structures in a simulation study. Furthermore, we analyze the prediction performance of these approaches on two gene expression microarray studies. The first study comprises patients with B-cell and T-cell type acute lymphoblastic leukemia and the second patients with acute megakaryoblastic leukemia. While the matching component can in principle guard against problematic observations, the combined approach is seen to neither improve identification of important covariates nor to improve prediction performance. Therefore, a combination of the two approaches cannot be recommended. Adjustment for potential confounders is seen to provide the best performance, i.e. a pure multivariable regression modeling strategy seems to be promising even in presence of considerable heterogeneity.     

最小交互熵在投入─产出表理论预测的应用

本文利用最小交互熵的概念和有关结论，直接地给出具有固定边际的投入─产出表预测模型的ＲＡＳ方法的结构形式；同时利用信息论中的不等式，直接地证明最小交互熵解就是对偶几何规划解；从而简单、明了地赋于投入─产出表ＲＡＳ方法的信息意义．我们还给出一种迭代计算方案．最后指出，该方法可以用统计方法检验新的理论预测与原投入─产出表的差异性．     

土壤污染程度分析及迁移规律的优化模型

本文根据题中给出的已知数据,对城区五个功能区的8种重金属元素（As、Cd、Cr、Cu、Hg、Ni、Pb、Zn）的浓度及分布范围,利用matlab软件中插值计算法和绘图功能,绘出了重金属元素在该城区的空间分布图．再运用变异系数值法初步估算出了每个功能区8种元素的变异系数值分析功能区重金属浓度超标情况,进而选用地累积指数法作优化模型,定量分析该城区内不同区域重金属的污染程度．根据采样的现有数据,分析了重金属在土壤中的传播特性,分析了重金属污染物在进入土壤环境系统后的迁移规律,阐述了土壤中重金属传播规律．     

Non-homogeneous solar models with metal enriched envelopes

Solar models with enhancement of heavy elements in the convective envelopes are investigated using the updated input physics. Unlike previous low-Z models that adopt quite low central metal abundance to considerably reduce neutrino fluxes, we investigate the effects of moderate enrichment of heavy elements in the solar convection zone on the solar structure and p-mode oscillations. It is found that the metal enriched models have less massive convection zones with deeper bottom boundaries, and their temperature profiles are systematically lower while the sound speed profiles are higher in the interior and lower in the envelope than that of the standard model. The contamination of heavy elements at different evolution phases is investigated, which results in little influence on the properties of the solar age models. The surface helium abundance is reduced considerably, and is able to approach the seismically determined value when the enhancement of heavy elements in the convection zone is carefully adjusted. The p-mode frequency patterns of our metal enriched models are systematically 10 μHz lower than those of the standard model, and are in better agreement with the results of observations.     

An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction

Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.     

金属矿山企业生产计划优化方法

针对金属矿山企业的单位开采与运输成本大、优化求解结果偏差大问题, 首先, 依据金属矿山企业编制开采计划的基本原则, 以矿石开采与运输成本最小化为优化目标, 利用整数规划方法, 构建了金属矿山企业生产计划数学模型, 其次, 为了精准快速求解金属矿山企业生产计划模型, 提出了改进的量子粒子群优化算法, 采用进化速度和聚集度因子对算法中的惯性权重进行动态调整, 并设计了双层可行域搜索策略, 提高了算法的局部和全局搜索能力。最后, 以某大型金属矿山企业采运生产作业为案例, 通过与矿山实际生产指标、非线性规划结果以及粒子群优化结果进行比较分析。结果表明:在相同经济指标和参数环境下, 本文算法优于其它两种优化方法, 且每吨矿石的开采和运输成本减少了0.05元左右, 降低了金属矿山企业的开采运输成本, 提高了企业的整体经济效益。