基因芯片数据的监督聚类分析

随着后基因组时代的到来,基因芯片技术越来越多地被应用到功能基因组的研究当中。如何快速有效地分析基因芯片实验所获得的大量生物学数据,成为当前一项具有重要意义的研究工作。监督聚类(supervised clustering analysis)是聚类分析的一种,它根据样本的先验信息或假设来决定样本的分类,并据此建立判别模型,继而利用该判别模型对未知对象进行分类。该方法已经成功应用到生物医学研究中的许多领域,成为分析基因芯片数据的重要手段。     

基因芯片整合方法在预报儿童急性髓性白血病亚型中的应用

对急性髓性白血病（AML）病人进行明确的亚型分类,有助于制定合适的治疗方案并预测其治疗效果。之前研究表明基因芯片技术在白血病亚型分类中已取得了较好效果,但由于儿童AML发病率较低,相应的芯片分析研究较少,因此目前用于构建儿童AML亚型分类模型的数据相对不足,是否可以应用现有的成人分类模型数据来对儿童AML进行预报还有待研究。应用基因芯片整合分析方法,对来自不同实验的研究成人或儿童AML亚型分类的基因芯片数据进行整合,应用支持向量机分析整合后数据集的亚型预报准确率。结果表明整合后的芯片数据在儿童AML亚型分类预报中的准确率达到97．24％,特征基因分析结果也说明在同一种AML亚型中,对于来自不同年龄组的样本,其特征基因有较高的表达相似性。     

基于不同分类模型的基因芯片癌症诊断方法研究

基因芯片技术的发展为生物信息学带来了机遇,使在基因表达水平上进行癌症诊断成为可能。但基因芯片数据高维小样本的特征也使传统机器学习方法面临挑战。本文利用真实的基因表达数据,测试了目前主要的分类方法和降维方法在癌症诊断方面的效果,通过实验对比发现：基于线性核函数的支持向量机可以有效地分类肿瘤与非肿瘤的基因表达,从而为癌症诊断提供借鉴。     

基于CART算法的肺癌微阵列数据的分类

基因芯片技术是基因组学中的重要研究工具。而基因芯片数据( 微阵列数据) 往往是高维的,使得降维成为微阵列数据分析中的一个必要步骤。本文对美国哈佛医学院 G. J. Gordon 等人提供的肺癌微阵列数据进行分析。通过 t- test,Wilcoxon 秩和检测分别提取微阵列数据特征属性,后根据 CART( Classification and Regression Tree) 算法,以 Gini 差异性指标作为误差函数,用提取的特征属性广延的构造分类树; 再进行剪枝找到最优规模的树,目的是提高树的泛化性能使得能很好适应新的预测数据。实验证明: 该方法对肺癌微阵列数据分类识别率达到 96% 以上,且很稳定; 并可以得到人们容易理解的分类规则和分类关键基因。     

基于AR模型的基因芯片数据识别

将自回归模型(AR)模型引入基因芯片数据识别领域,提出了基于自回归模型的时间序列特征提取方法．利用动态时轴弯曲(DTW)作为分类器,在标准的肿瘤基因芯片数据的识别结果表明,本方法能够达到100％的识别率,可以应用于基因芯片数据的识别、分类和基因疾病推断。     

Isomap的特点及其在基因芯片数据分析中的应用

基因芯片数据在本质上是非线性的,因此用线性数据分析方法处理基因芯片数据将不可避免的会带来偏差。全面分析非线性降维方法（Isomap）的技术特点以及将其应用到基因芯片数据分析中所需要注意的事项具有一定的意义。     

基因芯片技术在植物基因克隆中的应用研究进展

基因芯片是以预先设计的方式将大量的生物讯息密码(寡核苷酸、cDNA、基因组DNA等)固定在玻片、硅片等固相载体上组成的密集分子阵列.基因芯片技术本质是生物信号的平行分析,它利用核酸分子杂交原理,通过荧光标记技术检测杂交亲和与否,再经过计算机分析处理可迅速获得所需信息.由于其具有高通量、微型化、连续化、自动化、快速和准确等特点,已引起国际国内广泛的关注和重视,在许多领域得到了广泛的应用.本文简述了基因芯片的概念,技术特点及主要分类,着重对其在基因表达水平检测,基因突变和多态性的分析,基因组DNA分析,后基因组学研究以及转基因农作物检测等方面进行阐述,并说明其存在的问题及展望.     

基于熵信息处理和PCA的肿瘤基因表达谱分类识别

通过对基因表达谱数据的分析从而促进肿瘤诊断与治疗技术的发展,其研究正成为生物医学领域的一个热点。因此,提出了一种熵信息处理和主成分分析（principal component analysis,PCA）相结合的方法。首先运用熵信息对超高维基因表达谱数据进行粗选取,得到特征基因子集;由于基因子集仍存在相关性,进而利用PCA对其进一步冗余剔除;最后对得到的无冗余且具有正交性信息的基因特征进行真实数据实验。实验结果显示所采用的方法能有效去除肿瘤样本中的不相关和冗余信息,同时最大程度的保留肿瘤分类信息。与其他肿瘤分类方法相比,在精度上具有比较明显的优势,从而验证了该方法是有效的、可行的。     

一种基于类均值的肿瘤基因芯片数据的标准化方法

：分析了当前常用的标准化方法在肿瘤基因芯片中引起错误分类的原因,提出了一种基于类均值的标准化方法．该方法对基因表达谱进行双向标准化,并将标准化过程与聚类过程相互缠绕,利用聚类结果来修正参照表达水平．选取了5组肿瘤基因芯片数据,用层次聚类和K-均值聚类算法在不同的方差水平上分别对常用的标准化和基于类均值的标准化处理后的基因表达数据进行聚类分析比较．实验结果表明,基于类均值的标准化方法能有效提高肿瘤基因表达谱聚类结果的质量．     

基因芯片技术在植物研究中的应用

简述了基因芯片的原理、特点、分类及制作方法,同时详细综述了基因芯片技术在植物研究中的应用。     

Putting microarrays in a context: integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.     

Detecting unknown sequences with DNA microarrays: explorative probe design strategies

Designing environmental DNA microarrays that can be used to survey the extreme diversity of microorganisms existing in nature, represents a stimulating challenge in the field of molecular ecology. Indeed, recent efforts in metagenomics have produced a substantial amount of sequence information from various ecosystems, and will continue to accumulate large amounts of sequence data given the qualitative and quantitative improvements in the next-generation sequencing methods. It is now possible to take advantage of these data to develop comprehensive microarrays by using explorative probe design strategies. Such strategies anticipate genetic variations and thus are able to detect known and unknown sequences in environmental samples. In this review, we provide a detailed overview of the probe design strategies currently available to construct both phylogenetic and functional DNA microarrays, with emphasis on those permitting the selection of such explorative probes. Furthermore, exploration of complex environments requires particular attention on probe sensitivity and specificity criteria. Finally, these innovative probe design approaches require exploiting newly available high-density microarray formats.     

There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.     

Current advances in peptide and small molecule microarray technologies

Microarrays offer a compact solution for massively parallel screening. In recent years, microarrays have branched away from the exclusive pursuit of small molecule 'hits' in target centric screens, towards the sophisticated dissection of disease biology and comparative profiling of cellular states. This has led to innovative and instructive ways in which the platform may be deployed, providing new-found methods with which to harness the throughput achievable. Library design and diversity continues to drive success with peptide and small molecule microarrays. Newer synthesis and immobilization strategies extend the already wide repertoire of fabrication methods available. Herein we describe the latest advances in the small molecule and peptide microarray arena, which herald even more exciting breakthroughs in the coming decade.     

Protein microarrays and novel detection platforms

The field of proteomics has undergone rapid advancements over the last decade and protein microarrays have emerged as a promising technological platform for the challenging task of studying complex proteomes. This gel-free approach has found an increasing number of applications due to its ability to rapidly and efficiently study thousands of proteins simultaneously. Different protein microarrays, including capture arrays, reverse-phase arrays, tissue microarrays, lectin microarrays and cell-free expression microarrays, have emerged, which have demonstrated numerous applications for proteomics studies including biomarker discovery, protein interaction studies, enzyme-substrate profiling, immunological profiling and vaccine development, among many others. The need to detect extremely low-abundance proteins in complex mixtures has provided motivation for the development of sensitive, real-time and multiplexed detection platforms. Conventional label-based approaches like fluorescence, chemiluminescence and use of radioactive isotopes have witnessed substantial advancements, with techniques like quantum dots, gold nanoparticles, dye-doped nanoparticles and several bead-based methods now being employed for protein microarray studies. In order to overcome the limitations posed by label-based technologies, several label-free approaches like surface plasmon resonance, carbon nanotubes and nanowires, and microcantilevers, among others, have also advanced in recent years, and these methods detect the query molecule itself. The scope of this article is to outline the protein microarray techniques that are currently being used for analytical and function-based proteomics and to provide a detailed analysis of the key technological advances and applications of various detection systems that are commonly used with microarrays.     

染色体三维结构重建方法研究

染色体三维结构重构问题是近年生物领域中基因组学的热点研究问题,是以二维交互频率数据为基础来预测其三维空间结构。最新相关实验表明染色质的三维空间结构对于基因表达、调控等方面都具有重要意义。而Hi-c数据能利用染色质交互信息形成二维接触矩阵重构出染色体三维结构。本综述以染色体三维结构重建方法为研究对象,通过对染色体三维结构重建方法进行比较分析,综述了目前基于Hi-c数据在染色体三维结构重建中的经典方法,系统介绍了染色体三维结构重建技术的发展脉络,以促进染色体三维结构重建的进一步研究。     

Use of complex DNA and antibody microarrays as tools in functional analyses

While the deciphering of basic sequence information on a genomic scale is yielding complete genomic sequences in ever-shorter intervals, experimental procedures for elucidating the cellular effects and consequences of the DNA-encoded information become critical for further analyses. In recent years, DNA microarray technology has emerged as a prime candidate for the performance of many such functional assays. Technically, array technology has come a long way since its conception some 15 years ago, initially designed as a means for large-scale mapping and sequencing.The basic arrangement, however, could be adapted readily to serve eventually as an analytical tool in a large variety of applications. On their own or in combination with other methods, microarrays open up many new avenues of functional analysis.