基于关系代数的异构关系数据集成研究

网络上存在着大量的可用数据,且多是异构的。由于多种原因,用户很难获取自己需要的数据。数据集成技术为用户提供了一个统一的访问途径,以便获取分散在不同数据源上的数据。文中在对异构关系数据分析的基础上,结合实践中的例子,提出了基于关系代数的异构关系数据集成方法。把关系数据集成分为横向集成和纵向集成,给出了相关的集成理论和方法。将该方法应用于异构关系数据集成中,结果证明是有效的。     

一种面向领域WEB服务的数据中心模型

提出一种通用的面向领域WEB服务的数据中心模型.利用语义集成、数据映射、数据仓库及其他数据集成技术,不仅完成分布式异构数据源的无缝数据集成,而且实现数据源与数据中心之间的数据共享和透明数据交换,为领域WEB服务提供统一数据服务.在"油气生产系统软件集成平台"中采用该模型,组建了中国石油油气井生产领域数据中心,构建一个面向油气井生产领域,集生产管理、设备管理、工作流程控制、优化设计、故障诊断、辅助决策等功能为一体的WEB服务平台,解决了海量、分布式异构数据源的有机集成和无缝共享问题.从而验证了该模型的正确性及可行性.     

基于XML虚拟数据库的异构数据源集成模型研究

解决企业数据源异构需要一种公共的数据源模型给用户提供统一的用户视图,XML以其所具有的自描述性、灵活性、强大的数据交换能力等优势克服了其他数据模式的缺点。结合当前数据集成方面的技术,提出了一种基于Ⅺ∥几虚拟数据库的异构数据源集成模型,该模型很好地解决了异构数据源集成方面存在的一系列问题,并从数据模型和数据交换上阐述了该模型的可行性。剖析了该模型中的模式集成、异构数据集成视图、全局查询等。介绍了该模型在中国石油、QHSE信息系统中的成功应用。     

基于XML虚拟数据库的异构数据源集成模型研究

解决企业数据源异构需要一种公共的数据源模型给用户提供统一的用户视图,XML以其所具有的自描述性、灵活性、强大的数据交换能力等优势克服了其他数据模式的缺点.结合当前数据集成方面的技术,提出了一种基于XML虚拟数据库的异构数据源集成模型,该模型很好地解决了异构数据源集成方面存在的一系列问题,并从数据模型和数据交换上阐述了该模型的可行性.剖析了该模型中的模式集成、异构数据集成视图、全局查询等.介绍了该模型在中国石油QHSE信息系统中的成功应用.     

基于XML的高校数据交换平台的研究与设计

研究如何设计一个集成高校各部门之间异构数据源的数据交换平台。本着方便、适用的原则,经过多方比较,最终确定使用虚拟视图法中的Wrapper/Mediator系统方式进行设计,即在不同的数据源上增加一个XML中间件层,实现异构数据源与XML的相互转换,同时保持它们之间的映射关系。当用户提出请求,系统就将用户的请求组织成XML文档,提交给XML中间件层,用户对数据的访问通过XML中间件层的＂虚拟数据库＂实现,最终达到数据集成共享的目的。     

异构数据转换系统设计与实现

针对异构数据源分散存储给用户带来的不便,本文利用了ODBC数据转换技术和MD5算法数据校验,设计并实现了一个异构数据转换系统。该系统实现了txt、Excel、SQL数据库三种数据源间的相互转换和数据存储,保证了数据转换的正确性,达到了预期效果。     

基于SDO的异构数据集成研究与应用

为解决现存的异构数据集成解决方案中对异构数据处理没有统一的标准、硬编码多、集成系统扩展性差等问题,提出了基于SDO规范的异构数据集成方案。设计实现了一个统一的数据访问界面;利用数据访问服务封装了各种异构数据源,并将结果以同一种格式暴露给集成系统．实现了对数据源中数据访问、操作方式的统一;在查询分解方面,给出了基于数据源配置文件的查询分解方法,集成系统根据配置文件就可以与相应的数据源取得连接,而且只要修改相应的配置文件,就可以实现数据源的灵活修改;最后将该方案在基于WEB的PLM系统中进行了应用,验证了方案的可行性。实现结果表明该方案开发量小、扩展性好、效率高,能够很好地满足企业异构数据集成的需要。     

基于Web Services的语义异构数据集成设计与实现

高校在信息化建设过程中积累了大量异构、异质的数据源,如何将这些异构数据源中相关的数据资源进行有效整合是当前急需解决的问题.为此,提出了一种新的基于Web Services的异构数据集成解决方案,利用Web Services技术在异构数据集成中的优势,采用虚拟数据库法设计思想,通过设计领域字典表及字段映射表,有效地解决了异构数据集成中最难解决的语义异构问题,并从数据模型转换、全局查询等方面阐述了该方案的可行性.实验结果表明,该方案具有可用性和高效性.     

基于本体的数据集成系统的设计与实现

针对当前对异构数据源的数据集成中存在的语义异构问题,尤其是元素层的语义异构,通过分析现有的基于XML数据集成方法的基础上,将系统的设计分为三层,其中在数据层实现对异构数据源XML模式的包装,而在用户层和数据层之间增加一个中间层来实现全局查询到局部查询的转换。同时将本体论的思想引入集成系统框架,通过在中间层建立全局本体库和局部本体库,以及构建本体库之间的映射关系,解决了数据集成中异构数据源元素层的语义异构问题。     

基于Web服务的异构数据集成的研究

分析了异构数据库访问的技术及目前访问异构数据库系统存在的问题,提出在数据库管理系统前端增加中间数据源模块,数据交换双方共同约定好彼此需要交换的接口,设计了一个基于Web服务的异构数据集成方案,该方案提供给用户一个使用多种数据源的统一接口,消除了企业信息孤岛,解决了异构数据带来的一系列数据共享问题。     

蛋白质相互作用预测算法在帕金森病中的应用

对蛋白质相互作用的研究不仅能够理解生命的过程,也能为疾病治疗提供线索.通过对现有蛋白质相互作用预测计算方法的分析,将计算方法和生命科学相结合,在利用现有的知名生物数据库获得大量蛋白质相互作用关系数据的基础上,建立人类蛋白质相互作用网络,通过计算来预测可能导致帕金森病的蛋白质.在总结前人算法的基础上,利用改进的APM[1]算法,实现了对蛋白质一级网络以及二级网络的预测工作.     

High Throughput Mapping of Protein-Protein Interactions: Automation of the Yeast Two-Hybrid System

Protein-protein interactions are a vital component in both the function and regulation of virtually all biological processes. In the yeast two-hybrid system, one of the most commonly-used methods for studying protein-protein interactions, protein-protein interactions are detected by expression of a reporter gene driven by the interaction between bait and prey proteins. We report here the development of an automated system, the MegaMate, for carrying out high throughput (over 100,000 clones per day) yeast two-hybrid screens with full data tracking.     

Inductive logic programming for gene regulation prediction

We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.     

A survey of current trends in computational predictions of protein-protein interactions

Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the stateof-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.     

The current Salmonella-host interactome

Salmonella bacteria cause millions of infections and thousands of deaths every year. This pathogen has an unusually broad host range including humans, animals, and even plants. During infection, Salmonella expresses a variety of virulence factors and effectors that are delivered into the host cell triggering cellular responses through protein-protein interactions (PPI) with host cell proteins which make the pathogen's invasion and replication possible. To speed up proteomic efforts in elucidating Salmonella-host interactomes, we carried out a survey of the currently published Salmonella-host PPI. Such a list can serve as the gold standard for computational models aimed at predicting Salmonella-host interactomes through integration of large-scale biological data sources. Manual literature and database search of >2200 journal articles and >100 databases resulted in a gold standard list of currently 62 PPI, including primarily interactions of Salmonella proteins with human and mouse proteins. Only six of these interactions were directly retrievable from PPI databases and 16 were highlighted in databases featuring literature extracts. Thus, the literature survey resulted in the most complete interactome available to date for Salmonella. Pathway analysis using Ingenuity and Broad Gene Set Enrichment Analysis (GSEA) software revealed among general pathways such as MAPK signaling in particular those related to cell death as well as cell morphology, turnover, and interactions, in addition to response to not only Salmonella but also other pathogenic - viral and bacterial - infections. The list of interactions is available at http://www.shiprec.org/indicationslist.htm.     

蛋白质相互作用网络的相似子网搜索问题研究

蛋白质相互作用网络（Protein-Protein Interactions Network,PIN）的相似性问题是目前生物信息学领域研究的热点。将计算机科学和生物学相结合,提出了蛋白质相互作用网络邻居优先搜索算法。该算法综合蛋白质的序列信息和蛋白质相互作用网络的拓扑结构信息,适度提高与相似蛋白质有直接相互作用的蛋白质之间的相似系数,实现了不同物种间蛋白质相互作用相似子网络的搜索。与同类算法的对比实验表明,该算法可以处理更大规模的目标子网搜索,计算速度明显提高,且利用该算法获得的结果与目标子网具有更长的相似路径。论文采用该算法研究了酵母和果蝇的蛋白质相互作用网络,获得了10条相对保守的蛋白质相互作用（Protein-Protein Interactions,PPI）。     

动态加权蛋白质相互作用网络构建及其应用研究

一个蛋白质可能在不同条件或不同时刻与不同的蛋白质发生相互作用,这称为蛋白质的动态特性.蛋白质在分子处理的不同阶段参与到不同的模块,与其他的蛋白质共同完成某项功能.因此, 动态蛋白质相互作用的研究有助于提高蛋白质功能预测的准确率.结合蛋白质相互作用网络和时间序列基因表达数据,构建动态蛋白质相互作用网络.为降低PPI网络中假阴性对功能预测产生的负面影响,结合结构域信息和复合物信息,预测和产生新的相互作用,并对相互作用加权.基于构建的动态加权网络,提出一种功能预测方法D-PIN (Dynamic protein interaction networks). 基于三个不同的酵母相互作用网络实验结果表明, D-PIN 方法的综合性能比现有方法提高了14%以上.结果验证了构建的动态加权蛋白质相互网络的有效性.     

Predicting Protein-Protein Interactions by Association Mining

Identifying protein-protein interactions is a key problem in molecular biology. Currently, interactions cannot be reliably predicted on a proteome-wide scale but direct and indirect evidence for interactions is increasingly available from high-throughput interaction detection methods, gene expression microarrays, and protein annotation projects. In this paper we propose an association mining approach to integrating these diverse types of evidence. We apply this approach to a number of datasets consisting of interacting and non-interacting protein pairs annotated with different types of evidence. We identify patterns that distinguish interacting and non-interacting protein pairs, and use these patterns to assign a confidence level to proposed interactions.     

Predicting overlapping protein complexes in weighted interactome networks

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions.The increasing mount of protein–protein interaction(PPI)data has enabled us to predict protein complexes.In spite of the advances in these computational approaches and experimental techniques,it is impossible to construct an absolutely reliable PPI network.Taking into account the reliability of interactions in the PPI network,we have constructed a weighted protein–protein interaction(WPPI)network,in which the reliability of each interaction is represented as a weight using the topology of the PPI network.As overlaps are likely to have biological importance,we proposed a novel method named WN-PC(weighted network-based method for predicting protein complexes)to predict overlapping protein complexes on the WPPI network.The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes,and binds attachment proteins to candidate complexes.Finally,we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size.A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure,coverage rate,and P-value.We have applied WN-PC to two different yeast PPI data sets,one of which is a huge PPI network consisting of over 6000 proteins and 200000 interactions.Experimental results show that WN-PC outperforms the state-of-the-art methods.We think that our research may be helpful for other applications in PPI networks.