大肠杆菌启动子启动强度的预测

启动子是DNA序列中的关键元件,直接影响生物的转录与表达,启动子的研究对转录机制的阐明以及整个基因组功能的注释都具有重要作用。然而,用实验方法对启动子进行检测费时费力,发展启动子预测的方法具有十分重要的意义。本文基于离散小波变换建立伪三碱基组成表征DNA序列,支持向量机建模,预测大肠杆菌启动子的启动强度。首先采用二维映射法对DNA序列进行映射,得到二维离散的数字序列,并将之合并为一维数字序列;采用离散小波变换对数字映射序列进行转换,将得到的小波变换结果与三碱基组成结合构建伪三碱基组成,离散小波变换中小波函数与小波分解尺度的优化通过5-折交叉验证选取;构建得到的伪三碱基组成作为支持向量机的输入参数,建模进行预测。训练集得到的预测相关系数R为0.9830,RMSE为0.0907;测试集得到的预测相关系数R为0.8606,RMSE为0.1014。结果表明,模型的预测效果良好,说明基于离散小波变换的伪三碱基组成能够有效地反映DNA序列中碱基的顺序信息,本文方法不仅能够有效地实现大肠杆菌启动子启动强度的预测,也为DNA其他生物功能的预测提供了参考。     

基于改进马尔可夫模型的启动子预测算法

本文实现了基于马尔可夫模型的启动子预测算法，结合隐马尔可夫模型中的前向算法，改进了基于马尔可夫理论的启动子预测方法，具体改进了碱基转移概率的算法和序列所在模型的概率。改进的马尔可夫模型的预测结果显示，以此模型建立的系统能更有效地识别数据集中的三种序列。     

基于EM的启动子序列半监督学习

启动子的预测对于基因的定位有重要意义.已有多种对启动子进行预测的算法,涉及到信号搜索、内容搜索和CpG岛搜索等多种策略.基于马尔可夫模型的启动子分类方法也有研究,其中的转移概率都是直接通过统计已标号训练样本序列得来的.将半监督学习思想引入启动子序列分析中,推导出转移概率等参数的最大似然估计公式.实验中将待测试基因序列片段同已标号训练样本混合,利用得出的参数值对基因序列片段进行识别,使用少量的已标号的样本数据能得出较好的启动子识别结果.     

基于多特征融合的增强子-启动子相互作用预测综述

研究增强子-启动子相互作用机理有助于人们理解基因调控关系,进而揭示与疾病相关的基因,为疾病诊疗提供新思路和新方法。传统的生物检测方法的实验成本高、耗时长,且受分辨率的限制,难以精确鉴定单个增强子-启动子的相互作用。通过计算方法来解决生物问题已成为近年来的研究热点,此类方法可以通过复杂的网络结构主动学习序列特征和空间结构,进而准确预测增强子-启动子的作用。首先介绍了传统生物实验检测方法的研究现状;然后从序列特征的角度出发,围绕多特征融合的基本思想,对统计学和深度学习方法在增强子-启动子相互作用预测上的应用进行归纳整理;最后对该领域的研究热点和挑战进行总结分析。     

动态贝叶斯最小二乘支持向量机

为了对涡扇发动机的运行参数变化进行实时高精度预测,提出一种基于动态贝叶斯最小二乘支持向量机(LS-SVM)的时间序列预测算法。该算法将贝叶斯证据框架理论用于推断LS-SVM的初始模型参数;然后,利用样本增减迭代学习算法实现LS-SVM的参数动态调整。对某型涡扇发动机的摩擦力矩时间序列进行动态预测,并与动态LS-SVM模型的预测结果进行比较。结果显示,动态贝叶斯LS-SVM具有较好的预测精度。     

经验模态分解法在大气时间序列预测中的应用

介绍了一种可以提高非平稳时间序列预测精度的新方法, 该方法应用 Hilbert-Huang 变换的核心内容---经验模态分解法 (Empirical mode decomposition, EMD) 对非平稳时间序列进行分解, 以降低被预测信号中的非平稳性, 利用神经网络对分解后的各分量进行预测, 再将预测结果叠加. 利用该方法对石家庄市年逐月降水量进行预测, 预测结果显示, 其预测精度比直接用神经网络预测的预测精度有较明显的提高.     

基于小波非线性自回归网络的水文预测模型

不同时间尺度上的水文序列预测在水资源调配和防洪减灾决策中起着重要的作用。提出了一种基于小波分解和非线性自回归神经网络相结合的水文时间序列预测模型(WNARN)。运用Daubechies 5(db5)离散小波将水文序列数据分解为低频和高频子序列,作为非线性自回归神经网络模型(NARN)的输入变量,贝叶斯正则化优化算法用来泛化网络,训练模型对各子序列进行模拟预测,预测值经db5小波重构后得到原序列预测值。利用渭河流域三个水文站40多年的月径流量序列对所提出的WNARN模型进行验证和向前48步的预测能力测试,并与单一NARN模型的验证和预测结果进行对比。结果显示在相同的网络结构下所提出的方法能够显著提高水文序列的预测精度、预测周期及对重大水文事件的预测性,具有较高的泛化能力。     

分子模拟研究不同随机RNA片段适体与大肠杆菌紧密黏附素的相互作用

为了促进开发大肠杆菌快速检测适体生物传感器,通过对已知RNA-蛋白质相互作用原理和复合物结构的分析,在对相关文献资料和基于分子模拟技术的网络资源充分了解的基础上,模拟预测研究了随机RNA序列与肠致病性大肠杆菌紧密黏附素蛋白的相互作用。结果表明,RNA高级结构主要依赖于其一级结构的序列信息。NPDock模拟不同随机RNA序列与紧密黏附素相互作用时,不同长度RNA序列均可与紧密黏附素发生相互作用,但作用位点和相互位置有一定差异;对于相同长度不同排布的RNA序列,相互作用的差异性主要与序列排布信息有关。对于分子模拟研究RNA-紧密黏附素相互作用方法的可行性,通过RNA-蛋白质相互作用位点在线预测方法(PRIdictor)进行验证,结果表明,预测出的蛋白质、RNA相互作用位点均位于相互作用预测结构的接触面上,说明对于RNA-蛋白质相互作用的模拟预测研究方法具有一定的可行性,将有助于通过设计合成RNA改进适体筛选、研发的相关生物技术推广,以及应用创新。     

联合EMD和FSVM的非平稳时间序列预测

提出一种基于经验模态分解(EMD)和模糊支持向量机(FSVM)的非平稳时间序列组合预测方法。首先,利用EMD对非平稳时间序列进行分解,将其分解为时间尺度特征较为单一的单模态分量,降低待预测信号的非线性复杂度;然后,利用模糊支持向量机对EMD分解后的各固有模态函数进行预测;最后将各固有模态函数独立预测的结果进行叠加,即可得到原始序列的预测值。以带噪声的Lorenz系统和太阳黑子月平滑值序列为实验数据,对提出的预测方法进行了仿真分析。实验结果表明,与BP神经网络预测和传统的SVM预测方法相比,提出的方法具有更好的预测精度,而且对带有孤立点、噪声的序列信号具有较强的适应能力。     

改进鲸鱼算法构建混合模型的建筑能耗预测

建筑能耗数据具有非平稳和非线性特征,单一预测模型很难对其进行精准预测,提出一种用于建筑能耗短期预测的新型混合模型。利用互补集合经验模态分解方法(CEEMD)将波动性较大的能耗数据分解为一组本征模态函数和一个残差序列;基于反向学习、差分进化算法并引入控制参数对鲸鱼优化算法(WOA)进行改进,有效解决算法早熟收敛与陷入局部最优等问题,提出改进算法UWOA(upgraded whale optimization algorithm);利用UWOA优化Elman神经网络的权值与阈值,优化后的Elman神经网络对本征模态函数和残差序列进行预测并集成,得到能耗预测值。应用CEEMD-UWOA-Elman混合模型对上海某大型公共建筑能耗进行短期预测,结果显示混合模型获得很好的预测效果。     

带膜分裂和促进剂的通讯膜系统求解QSAT问题

膜计算是自然计算的一个分支,膜计算中所研究的模型均称为膜系统,而细胞间通讯是膜系统的一个重要特征。带膜分裂的通讯膜系统是一种分布式并行计算模型,可以在多项式时间内解决计算困难问题。文中将促进剂引入带膜分裂的类细胞型通讯膜系统,提出了膜系统的一种变型——带膜分裂和促进剂的通讯膜系统,其中,一个促进剂可以同时控制多条规则,而促进剂本身不参与该条规则的进化。文中研究了带膜分裂和促进剂的通讯膜系统的计算效率,证明该类膜系统在使用同向规则长度为2,每条规则中促进剂的个数最多为1时,可以在多项式时间内求解PSPACE完全问题(QSAT问题)的统一解。     

A biological multiplexer,designs, and simulations

Biological circuits can be used in synthetic biology to perform logical functions similar to those observed in electronic circuits. These circuits are applied as a method to define cellular functions in useful ways. The purpose of this field of research is to design and simulate systems which can combine VLSI technology and biological circuits. In this regard, five different architectures (AND–OR, AND–NOR–OR–AND Full-NAND, and Full-NOR) of a new biomultiplexer have been designed and simulated. The results of these architectures are compared in terms of delay as well as the number of promoters and genes. The two-level architectures AND–OR, AND–NOR, and OR–AND, in addition to the greater number of genes and promoters, have also a longer execution time than the designs with two universal NAND or NOR gates. In Full-NAND design, the execution time is reduced by lowering the number of genes and the number of promoters. Finally, Full-NOR design offers the shortest execution time of 0.612 s, while reducing promoters by 64% and the number of genes by 50%, compared to other proposed architectures.

     

A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information

The aim of this study is to introduce a novel generalized distance measure for interval valued intuitionistic fuzzy sets and to illustrate the applicability of the proposed distance measure to group decision making problems. Firstly, a generalized distance measure is proposed along with proofs satisfying its axioms. Then, a comparison between the proposed distance measure and well-known distance measures is performed in terms of counter-intuitive cases. Subsequently, the extension of TOPSIS method, in which the proposed distance measure is used to calculate separation measures, to an interval valued intuitionistic fuzzy (IVIF) environment is demonstrated to solve multi-criteria group decision making (MCGDM) problems using optimal criteria weights determined with linear programming model based on the concept of maximizing relative closeness coefficient. Finally, two illustrative examples are provided for proof-of-concept purposes and to demonstrate benefits of using the proposed distance measure over the existing ones in IVIF TOPSIS method for MCGDM problems.     

一个基于三角函数的直觉模糊熵公式

利用三角函数定义了一个直觉模糊熵公式,该公式不仅考虑了直觉模糊集的隶属度与非隶属度的偏差,而且考虑了直觉模糊集的犹豫度.对以往文献给出的两个直觉模糊熵公式进行了讨论,并将所提出的公式与这两个公式进行了比较.算例分析表明,所提出的熵公式能够反映直觉模糊集的不确定性和未知性程度.     

A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system

We have investigated the real-world task of recognizing biological concepts in DNA sequences in this work. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on feature selection (FS) and Artificial Immune Recognition System (AIRS) with Fuzzy resource allocation mechanism (Fuzzy-AIRS), which is first proposed by us. The aim of this study is to improve the prediction accuracy of Escherichia coli promoter gene sequences using a novel system based on FS and Fuzzy-AIRS. The E. coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have reduced the dimension of E. coli promoter gene sequences dataset from 57 attributes to 4 attributes by means of FS process. Second, Fuzzy-AIRS classifier algorithm has been run to predict the E. coli promoter gene sequences. The robustness of the proposed method is examined using prediction accuracy, sensitivity and specificity analysis, k-fold cross-validation method and confusion matrix. Whilst only Fuzzy-AIRS classifier has obtained 50% prediction accuracy using 10-fold cross-validation, the proposed system has obtained 90% prediction accuracy in the same conditions. These obtained results have indicated that the proposed system obtain the success rate in recognizing promoters in strings that represent nucleotides.     

A dissimilarity measure based on Jensen Shannon divergence measure

The need of suitable measures to find the distance between two probability distributions arises as they play an eminent role in problems based on discrimination and inferences. In this communication, we have introduced one such divergence measure based on well-known Shannon entropy and established its existence. In addition to this, a new dissimilarity measure for intuitionistic fuzzy sets corresponding to proposed divergence measure is also introduced and validated. Some major properties of the proposed dissimilarity measure are also discussed. Further, a new multiple attribute decision-making (MADM) method based on the proposed dissimilarity measure is introduced by using the concept of TOPSIS and is thoroughly explained with the help of an illustrated example on supplier selection problem. Finally, the application of proposed dissimilarity measure is given in pattern recognition and the performance is compared with some existing divergence measures in the literature.     

A dissimilarity Jensen–Shannon divergence measure for intuitionistic fuzzy sets

The need of suitable divergence measures arise as they play an important role in discrimination of two probability distributions. The present communication is devoted to the introduction of one such divergence measure using Jensen inequality and Shannon entropy and its validation. Also, a new dissimilarity measure based on the proposed divergence measure is introduced. Besides establishing validation, some of its major properties are also studied. Further, a new multiple attribute decision making method based on a proposed dissimilarity measure is introduced and is thoroughly explained with the help of an illustrated example. The paper is summed up with an application of the proposed dissimilarity measure in pattern recognition.     

核密度估计的聚类算法*

相似性度量是聚类分析的重要基础,如何有效衡量类属型符号间的相似性是相似性度量的一个难点.文中根据离散符号的核概率密度衡量符号间的相似性,与传统的简单符号匹配及符号频度估计方法不同,该相似性度量在核函数带宽的作用下,不再依赖同一属性上符号间独立性假设.随后建立类属型数据的贝叶斯聚类模型,定义基于似然的类属型对象-簇间相似性度量,给出基于模型的聚类算法.采用留一估计和最大似然估计,提出3种求解方法在聚类过程中动态确定最优的核带宽.实验表明,相比使用特征加权或简单匹配距离的聚类算法,文中算法可以获得更高的聚类精度,估计的核函数带宽在重要特征识别等应用中具有实际意义.     

两竞争推广商与酒店的合作协调研究

随着酒店市场竞争的日趋激烈,酒店为了提高入住率,纷纷与第三方推广商开展合作.酒店如何通过与推广商合作来提高利润率成为酒店业重点关注的话题.本文分析市场上存在一个占主导地位的酒店以及和酒店合作的两家推广商,利用博弈论构建了酒店和推广商的利润函数模型.为此,首先分析了分散决策下博弈三方的最优策略,进而分析了集中决策下三方的最优策略,并刻画了影响酒店和推广商最优策略的影响因素.并进一步提出了能实现整体最优并能实现双方Pareto改善的协调契约.本文的研究发现以下现象:1)酒店的价格增加,两推广商的利润均有提升;2)当推广商间的竞争激烈程度增大时,推广商会压低利润水平并提升推广力度.从而,酒店的利润会不断提升,并且供应链的协调程度也在不断增加.数值例子也验证了所设计的协调契约对整体协调的有效性.