基于数字签名与Trie的保序子矩阵约束查询

目前基因芯片技术飞速发展,促使生物学家积累了大量的在不同实验条件下的基因表达数据.事实证明基因芯片数据分析在理解基因功能、基因调控和分子生命过程中发挥着重要作用.保序子矩阵（OPSM）是基因芯片数据分析技术中一种有效的模型,其可以发现在部分基因和不同实验条件下具有相同表达趋势的聚类.在分析基因表达机理过程中,OPSM的检索无疑节省了生物学家的时间与精力.目前OPSM的查询主要是基于关键词的检索方法,但是分析者对结果具有微弱的控制力.通常分析者所能决定临时的参数设置往往偏离其领域知识,致使检索结果与真实想要结果相去甚远.为了解决上述问题,本文提出两类基于数字签名与Trie的OPSM索引与约束查询方法.在真实数据上进行了大量的实验,实验结果表明所提出方法具有良好的有效性与可扩展性.     

Gene expression time series modeling with principal component and neural network

In this work, gene expression time series models have been constructed by using principal component analysis (PCA) and neural network (NN). The main contribution of this paper is to develop a methodology for modeling numerical gene expression time series. The PCA-NN prediction models are compared with other popular continuous prediction methods. The proposed model can give us the extracted features from the gene expressions time series and the orders of the prediction accuracies. Therefore, the model can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. Based on the results of two public real datasets, the PCA-NN method outperforms the other continuous prediction methods. In the time series model, we adapt Akaike's information criteria (AIC) tests and cross-validation to select a suitable NN model to avoid the overparameterized problem.     

A permutation test motivated by microarray data analysis

We introduce a nonparametric test intended for large-scale simultaneous inference in situations where the utility of distribution-free tests is limited because of their discrete nature. Such situations are frequently dealt with in microarray analysis where the number of tests is much larger than the sample size. The proposed test statistic is based on a certain distance between the distributions from which the samples under study are drawn. In a simulation study, the proposed permutation test is compared with permutation counterparts of the t-test and the Kolmogorov–Smirnov test. The usefulness of the proposed test is discussed in the context of microarray gene expression data and illustrated with an application to real datasets.     

HMiner: Efficiently mining high utility itemsets

High utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets. This paper advances the state-of-the-art and presents HMiner, a high utility itemset mining method. HMiner utilizes a few novel ideas and presents a compact utility list and virtual hyperlink data structure for storing itemset information. It also makes use of several pruning strategies for efficiently mining high utility itemsets. The proposed ideas were evaluated on a set of benchmark sparse and dense datasets. The execution time improvements ranged from a modest thirty percent to three orders of magnitude across several benchmark datasets. The memory consumption requirements also showed up to an order of magnitude improvement over the state-of-the-art methods. In general, HMiner was found to work well in the dense regions of both sparse and dense benchmark datasets.     

基于基因表达式编程的多目标自动聚类算法

聚类是将物理或抽象对象的集合分成由类似的对象组成的多个类(簇)的过程.同一个簇中的对象彼此相似,而不同簇中的对象差异较大.以基因表达式编程算法为基础,结合新设计的广义聚类代数算子和目标优化函数,提出一种基于基因表达式编程的多目标自动聚类算法(MAGEP-Cluster).该算法不仅可以自动确定最优聚类的数目,还可以同时...     

基因表达数据的低秩投影最小二乘回归子空间分割*

基因表达数据具有高维、小样本、多噪声和高冗余的特点,使传统聚类方法效率较低.子空间分割是高维数据聚类的有效手段,但直接对基因表达数据进行子空间分割会降低聚类性能.为了更有效地聚类,文中提出低秩投影最小二乘回归子空间分割方法.首先利用改进的低秩方法将数据投影至潜在子空间,以便去除数据中可能的毁损,得到较干净的数据字典.然后采用最小二乘回归方法获得数据低维表示并构造仿射矩阵,利用该仿射矩阵实现聚类.在6个公开基因表达数据集上的实验表明文中方法的有效性.     

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.     

Feature clustering based support vector machine recursive feature elimination for gene selection

In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the curse of dimensionality. To get good classification performance, it is necessary to preprocess the gene expression data. Support vector machine recursive feature elimination (SVM-RFE) is a classical method for gene selection. However, SVM-RFE suffers from high computational complexity. To remedy it, this paper enhances SVM-RFE for gene selection by incorporating feature clustering, called feature clustering SVM-RFE (FCSVM-RFE). The proposed method first performs gene selection roughly and then ranks the selected genes. First, a clustering algorithm is used to cluster genes into gene groups, in each which genes have similar expression profile. Then, a representative gene is found to represent a gene group. By doing so, we can obtain a representative gene set. Then, SVM-RFE is applied to rank these representative genes. FCSVM-RFE can reduce the computational complexity and the redundancy among genes. Experiments on seven public gene expression datasets show that FCSVM-RFE can achieve a better classification performance and lower computational complexity when compared with the state-the-art-of methods, such as SVM-RFE.     

面向函数依赖的隐私保护研究

信息技术的发展为人们生活带来便利的同时也带来了个人隐私泄露的风险,数据匿名化是阻止隐私泄露的有效方法。然而,已有的匿名化方法主要考虑切断准标识符属性和敏感属性之间的关联,而没有考虑准标识符属性之间,以及准标识符属性和敏感属性之间存在的函数依赖关系。针对隐私保护的数据发布中存在的问题,研究数据之间存在函数依赖时,如何有效保护用户的隐私信息。首先针对数据集中存在函数依赖情况,提出(l,α)-多样性隐私保护模型;其次,为更好地实现用户隐私保护以及数据效用的增加,提出结合扰动和概化/隐匿的杂合方法实现匿名化算法。最后,实验验证了算法的有效性和效率,并对结果做了理论分析。     

TBM,a transformation based method for microaggregation of large volume mixed data

Due to recent advances in data collection and processing, data publishing has emerged by some organizations for scientific and commercial purposes. Published data should be anonymized such that staying useful while the privacy of data respondents is preserved. Microaggregation is a popular mechanism for data anonymization, but naturally operates on numerical datasets. However, the type of data in the real world is usually mixed i.e., there are both numeric and categorical attributes together. In this paper, we propose a novel transformation based method for microaggregation of mixed data called TBM. The method uses multidimensional scaling to generate a numeric equivalent from mixed dataset. The partitioning step of microaggregation is performed on the equivalent dataset but the aggregation step on the original data. TBM can microaggregate large mixed datasets in a short time with low information loss. Experimental results show that the proposed method attains better trade-off between data utility and privacy in a shorter time in comparison with the traditional methods.     

High-Performance Algorithm Engineering for Computational Phylogenetics

A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool in biological, pharmaceutical, and medical research. Phylogeny reconstruction from molecular data is very difficult: almost all optimization models give rise to NP-hard (and thus computationally intractable) problems. Yet approximations must be of very high quality in order to avoid outright biological nonsense. Thus many biologists have been willing to run farms of processors for many months in order to analyze just one dataset. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing phylogenetic algorithms, as well as help designers produce better algorithms. We present an overview of algorithm engineering techniques, illustrating them with an application to the breakpoint analysis method of Sankoff et al., which resulted in the GRAPPA software suite. GRAPPA demonstrated a speedup in running time by over eight orders of magnitude over the original implementation on a variety of real and simulated datasets. We show how these algorithmic engineering techniques are directly applicable to a large variety of challenging combinatorial problems in computational biology.     

VizCluster and its Application on Classifying Gene Expression Data

Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. A visualization can provide a qualitative overview of large and complex datasets, can summarize data, and can assist in identifying regions of interest and appropriate parameters focused on quantitative analysis. Recently, DNA microarray technology provides a broad snapshot of the state of the cell, by measuring the expression levels of thousands of genes simultaneously. Such information can thus be used to analyze different samples by gene expression profiles. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analyze, and visualize these fast growing data.In this paper, we present a dynamic interactive visualization environment, VizCluster, and its application on classifyinggene expression data. VizCluster takes advantage of graphical visualization methods to reveal underlining data patterns. It combines the merits of both high dimensional projection scatter-plot and parallel coordinate plot. In its core lies a nonlinear projection which maps the n-dimensional vectors onto two-dimensional points. To preserve the information at different scales and yet reduce the typical problem of parallel coordinate plots being messy caused by overlapping lines, a zip zooming viewing method is proposed. Integrated with other features, VizCluster is developed to give a simple, fast, intuitive, and yet powerful view of the data set. Its primary applications are on the classification of samples and evaluation of gene clusters for microarray datasets. Three gene expression datasets are used to illustrate the approach. We demonstrate that VizCluster approach is promising to be used for analyzing and visualizing microarray data sets and further development is worthwhile.     

基于自主学习与SCAD-Net正则化的回归模型

众多基因生物标志物选择方法常因研究样本较少而不能直接用于临床诊断.于是有学者提出整合不同基因表达数据同时保留生物信息完整性的方法.然而,由于存在批量效应,导致直接整合不同基因表达数据可能会增加新的系统误差.针对上述问题,提出一个融合自主学习与SCAD-Net正则化的分析框架.一方面,自主学习方法能够先从低噪声样本中学习出基础模型,然后再通过高噪声样本学习使得模型更加稳健,从而避免批量效应;另一方面,SCAD-Net正则化融合了基因表达数据与基因间的交互信息,可以实现更好的特征选择效果.不同情形下的模拟数据以及在乳腺癌细胞系数据集上的结果表明,基于自主学习与SCAD-Net正则化的回归模型在处理高维复杂网络数据集时具有更好的预测效果.     

基于ReliefF和改进乌鸦搜索优化的并行入侵检测方法

针对微阵列基因表达数据高维小样本、高冗余且高噪声的问题,提出一种基于FCBF特征选择和集成优化学习的分类算法FICS-EKELM。首先使用快速关联过滤方法FCBF滤除部分不相关特征和噪声,找出与类别相关性较高的特征集合;其次,运用抽样技术生成多个样本子集,在每个训练子集上利用改进乌鸦搜索算法同步实现最优特征子集选择和核极限学习机KELM分类器参数优化;然后基于基分类器构建集成分类模型对目标数据进行分类识别;此外运用多核平台多线程并行方式进一步提高算法计算效率。在六组基因数据集上的实验结果表明,本文算法不仅能用较少特征基因达到较优的分类效果,并且分类结果显著高于已有和相似方法,是一种有效的高维数据分类方法。     

Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly.     

Ensemble gene selection for cancer classification

Cancer diagnosis is an important emerging clinical application of microarray data. Its accurate prediction to the type or size of tumors relies on adopting powerful and reliable classification models, so as to patients can be provided with better treatment or response to therapy. However, the high dimensionality of microarray data may bring some disadvantages, such as over-fitting, poor performance and low efficiency, to traditional classification models. Thus, one of the challenging tasks in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data that can directly contribute to the phenotype or symptom of disease. In this paper, we propose a new ensemble gene selection method (EGS) to choose multiple gene subsets for classification purpose, where the significant degree of gene is measured by conditional mutual information or its normalized form. After different gene subsets have been obtained by setting different starting points of the search procedure, they will be used to train multiple base classifiers and then aggregated into a consensus classifier by the manner of majority voting. The proposed method is compared with five popular gene selection methods on six public microarray datasets and the comparison results show that our method works well.     

Community-preserving anonymization of graphs

In this paper, we propose a novel edge modification technique that better preserves the communities of a graph while anonymizing it. By maintaining the core number sequence of a graph, its coreness, we retain most of the information contained in the network while allowing changes in the degree sequence, i. e. obfuscating the visible data an attacker has access to. We reach a better trade-off between data privacy and data utility than with existing methods by capitalizing on the slack between apparent degree (node degree) and true degree (node core number). Our extensive experiments on six diverse standard network datasets support this claim. Our framework compares our method to other that are used as proxies for privacy protection in the relevant literature. We demonstrate that our method leads to higher data utility preservation, especially in clustering, for the same levels of randomization and k-anonymity.     

Promoter analysis of co-regulated genes in the yeast genome.

The use of high density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of more than 6000 genes in the yeast genome. In an effort to build a comprehensive Yeast Promoter Database and to develop new computational methods for mapping upstream regulatory elements, we started recently in an on going collaboration with experimental biologists on analysis of large-scale expression data. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements are important information for our understanding of combinatorial control of gene expression. As a bioinformatics attempt in this new direction, we have done some computational exploration of various initial experimental data. We will use cell-cycle regulated gene expression as a specific example to demonstrate how one may extract promoter information computationally from such genome-wide screening. Full report of the experiments and of the complete analysis will be published elsewhere when all the experiments are to be finished later in this year (Spellman, P.T., et al. 1998. Mol. Biol. Cell 9, 3273-3297).     

BiDMAP - A Biological Data Management and Analysis Platform

With the rapid development of biotechnology, more and more genomic and associated functional data are available. It is necessary and possible for biologists and bioinformaticians to integrate various databases in order to mine biological knowledge.Moreover, the complexity of huge amount of biological data and their complicated structure need an advanced management system which can store and manage data efficiently, and provide subject-oriented analysis tools for further study. BiDMAP is a platform designed to meet these needs. The core of BiDMAP is based on an Object Relational Database Management System (ORDBMS) which makes it possible not only to store and manage biological data but also to implement analysis tools on a single platform. The goal of BiDMAP is to integrate different datasets on specific subjects into one platform and provides a unified, easy-to-learn, easy to-use interface to access those data. Complicated data structures are viewed as objects, stored in the database and manipulated viastandard SQL. BiDMAP presents an intuitive approach for biologists with little computational skills and leaves the flexibility for further development. This paper gives an overview of the design and implementation of BiDMAP and gives a simple analysis example - finding interleukin candidates with implemented analysis tool of pattern scan.