An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features (genes), they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter (q₀) in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.     

两种过滤特征基因选择算法的有效性研究

对基因表达谱进行特征基因选择不仅能改善疾病分类方法的效能,而且为寻找与疾病相关的特征基因提供新的途径．通过比较用调整p值的t检验、非参数评分两种特征基因选择算法后和未进行选择时支持向量机(SVM)分类器的分类性能、支持向量(SV)的吻合度、错分样本ID的吻合度和对样本均匀翻倍后的稳定性．结果发现：特征选择后线性、核函数为二阶多项式和径向基的SVM分类性能明显提高;特征选择前后的SV及错分样本ID的吻合度均较高;SVM的稳定性较好．由此得出结论：这两种特征选择算法具有一定的有效性．     

Simulating the Folding Pathway of RNA Secondary Structure Using the Modified Ant Colony Algorithm

<正> A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial stage.Furthermore,a more realistic formula is used to compute the energy ofmulti-branch loop in the following iteration.Then a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different stems.Finally by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values forparameters.The measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.     

以木糖异构酶基因为筛选标记的玉米遗传转化

利用木糖异构酶基因作为筛选标记可以在含有不同浓度木糖的培养基上筛选出玉米再生植株,其中50％-100％木糖浓度的总体筛选效果较好,但不同玉米基因型之间筛选的最佳浓度差异很大。通过DNA点杂交、PCR及PCR．Southern印记法检测表明,木糖异构酶基因已经整合到转基因植株中。以木糖作为筛选剂,可以减小潜在的生物安全隐患。     

Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network

This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network （VANET）. A novel bionic swarm intelligence algorithm, which is called ant colony algorithm, was introduced into a traditional ad-hoc route algorithm named AODV. Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units, the parameters in ant colony system were modified to enhance the performance of the route selection probability rules. When the vehicle moves into the range of several different roadsides, it could build the route by sending some route testing packets as ants, so that the route table can be built by the reply information of test ants, and then the node can establish the optimization path to send the application packets. The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm, especially when the vehicle is in higher speed or the number of nodes increases.     

基于小波低频系数基因芯片数据的特征提取

特征提取和分类是模式识别中的关键问题。结合小波分析理论和支持向量机理论,构造分类器模型,将前列腺癌基因芯片数据分成癌症和正常两种。提取小波低频系数表征原始数据并送入支持向量机分类器分类,实验证明:提取db1小波4层分解下的低频系数,送入分类器分类后正确分类率达到93.53%。Haar小波的正确率是92.94%。可见提取不同小波低频系数,得到的分类效果相差不大。     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.     

以新霉素抗性基因突变体为筛选标志的真核表达载体的构建

新霉素抗性基因(neo)是真核表达载体的常用筛选标志neo基因编码新霉素磷酸转移酶Ⅱ（NPT Ⅱ）,能催化G418、卡那霉素等多种氨基糖苷抗生素分子磷酸化而使之失去抗菌活性。通过对真核表达载体的筛选标志基因neo进行定点突变,以降低NPTⅡ的活性,然后用含neo突变体的真核表达载体pmDNA构建荧光素酶表达质粒,稳定转染CHO-K1细胞,发现表达荧光素酶的阳性细胞比例达到95%,其中高表达细胞集落的筛选率明显高于对照组。     

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.     

Ant colony optimization as a method for strategic genotype sampling

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK_P), the probability of assigning true alleles (AK_G) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A⁻¹). Comparisons of these two methods showed that ACO yielded an increase in AK_P ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A⁻¹, with different pedigree sizes and structures.     

利用微卫星标记鉴定水稻的稻瘟病抗性

应用水稻稻瘟病抗性基因Pid(t)紧密连锁的微卫星标记RM262对含有该抗病基因的品种地谷与感病品种江南香糯和8987的杂交F2群体进行遗传分析和抗性鉴定,结果表明,RM262的PCR扩增物在抗、感品种之间的多态性较好;在2个F2群体中,RM262和抗病基因间的重组率分别为5.74%和8.17%,应用该标记的抗性纯合和杂合带型选择抗性植株,其准确率可达98%以上。此外,还就分子标记辅助育种进行了讨论。     

应用于乳酸菌的非抗生素抗性选择标记系统

 乳酸菌是一类重要的安全型微生物,在免疫载体疫苗开发及食品菌株改良等医疗、食品领域均有广泛的应用.非抗生素抗性选择标记是乳酸菌基因工程菌株构建中必不可少的关键组成部分,也是目前乳酸菌研究的前沿和热点.根据筛选时质粒和受体菌之间的表型关系及特征,主要分为显性选择标记、互补型选择标记、显性/互补型选择标记、双质粒选择标记4大类.其中显性选择标记中的细菌素抗性/免疫性选择标记及互补型选择标记中的糖类选择标记均有较大的发展潜力及应用空间;双质粒选择标记系统构建的筛选过程新颖独特,为整个选择标记系统的发展开辟了新的途径及思路.     

利用基因芯片对一高保守基因进行肿瘤相关分析

本对大规模人类cDNA测序过程中获得的一条高保守基因进行了初步功能研究,生物信息学研究发现该基因在人类、小鼠、果蝇、拟南芥和裂殖酶母中都有很高的保守性,其他分析预测该基因可能具有肿瘤相关性。RT－PCR分析表明,该基因在成人和胎儿组织中广泛谱表达。利用基因芯片分析该基因在7例肝癌、5例胰腺癌、2例喉癌和2例肺癌中表达情况,结果证实了该基因的肿瘤相关性,并且提示该基因在不同类型中可能处于不同的地位。     

SDED: a novel filter method for cancer-related gene selection

Gene selection is to detect the most significantly expressed genes under different conditions expression data. The current challenge in gene selection is the comparison of a large number of genes with limited patient samples. Thus it is trivial task in simple statistical analysis. Various statistical measurements are adopted by filter methods applied in gene selection studies. Their ability to discriminate phenotypes is crucial in classification and selection. Here we describe the standard deviation error distribution (SDED) method for gene selection. It utilizes variations within-class and among-class in gene expression data. We tested the method using 4 leukemia datasets available in the public domain. The method was compared with the GS2 and CHO methods. The Prediction accuracies by SDED are better than both GS2 and CHO for different datasets. These are 0.8-4.2% and 1.6-8.4% more that in GS2 and CHO. The related OMIM annotations and KEGG pathways analyses verified that SDED can pick out more 4.0% and 6.1% genes with biological significance than GS2 and CHO, respectively.     

基于小波高频系数基因芯片数据的特征提取

结合小波分析理论与支持向量机理论,构造分类器模型,将前列腺癌基因芯片数据分成癌症和正常两种。本文着重研究小波高频系数基因芯片数据的特征提取,并通过实验对比小波高频系数和低频系数特征提取对分类器性能的影响。其中haar小波3层分解提取高频系数,送入分类器分类后,得到的正确分类率为93.31%。db1小波4层分解提取低频系数,送入分类器分类后,得到的正确分类率为93.53%。小波低频系数特征提取分类效果总体上好于高频系数,分类器性能稳定。     

以木糖异构酶基因xylA为选择标记的Gateway系统植物表达载体的构建

目的:构建以木糖异构酶基因xylA为筛选标记的无抗生素标记Gateway系统植物表达载体。方法:克隆大肠杆菌木糖异构酶基因xylA并用其替换植物表达载体pCAMBIA1301中的hpt基因,利用载体中的多克隆位点将Gateway Binary Vector(pH7WG2D)中酶切位点XbaⅠ和HindⅢ之间包括P35S、T35S、attR1、attR2和CmR-ccdB的片段重组入表达载体pCAMBIA1301中,构建表达载体pCAMBIA1301-xylA-GW,利用含有津田芜菁HY5基因片段的BP反应产物与载体进行LR反应,获得含有目的基因的植物表达载体pCAMBIA1301-xylA-HY5,并导入根癌农杆菌LBA4404中。结果:抗生素筛选及酶切和PCR鉴定表明成功构建了以xylA为筛选标记的无抗生素标记植物表达载体pCAMBIA1301-xylA-HY5。结论:利用木糖异构酶基因xylA结合Gateway克隆技术构建无抗生素标记植物表达载体,可简化、方便植物转基因表达载体构建。     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.