多媒体教育资源库层次索引模型

依据多媒体教育资源各层次特征及其关系,通过定义资源媒体特征空间,构建了教育资源库层次索引模型,各层次特征间的映射规则将资源的高层语义特征映射到低层媒体特征,达到了扩充描述语义的目的,提高了查全率;结合学科本体,规范了资源标注内容和用户查询条件的描述,减少了人为因素对查询项和索引项匹配相似度计算的影响,提高了查准率.     

基于Gene Ontology的表达谱与基因功能相似性的相关性研究

聚类是芯片数据分析中被广泛使用的方法。未知基因的功能通常通过其与已知基因在不同生物状态下具有表达相似性来进行预测。然而，还未有人就这种通过表达相似性来进行功能注释的方法的可靠性进行评估。本文利用Gene Ontology对表达相似性和基因功能相似性的相关关系进行了全面的研究。研究表明，尽管表达谱的相似性和基因功能相似性之间有一定的依赖关系，但相关性较弱。在Gene Ontology的三大类中，相对生物过程和分子功能，基因表达谱的相似性更有助于细胞组分的注释。本文的研究结果对于基因功能的预测有一定的指导意义。     

一种基于Web的分类体系学习算法

领域分类结构的抽取已成为本体工程和本体学习的关键部分,提出一种新的分类结构学习算法,将Web作为知识获取的语料库,运用迭代方法抽取相关语言学模式,再利用语言学模式抽取分类结构,并采用改进的互信息方法对结果进行评价和过滤,最后通过实验对该分类学习算法的性能进行评价.实验表明：算法具有良好的跨领域性,在准确率和召回率方面也有改善.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

Computational analysis for the determination of deleterious nsSNPs in human MTHFD1 gene

Single nucleotide polymorphisms (SNPs) are the most common genetic polymorphisms and play a major role in many inherited diseases. Methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) is one of the enzymes involved in folate metabolism. In the present study, the functional and structural consequences of nsSNPs of human MTHFD1 gene was analyzed using various computational tools like SIFT, PolyPhen2, PANTHER, PROVEAN, SNAP2, nsSNPAnalyzer, PhD-SNP, SNPs&GO, I-Mutant, MuPro, ConSurf, InterPro, NCBI Conserved Domain Search tool, ModPred, SPARKS-X, RAMPAGE, FT Site and PyMol. Out of 327 nsSNPs form human MTHFD1 gene, total 45 SNPs were predicted as functionally most significant SNPs, among which 17 were highly conserved and functional, 17 were highly conserved and structural residues. Among 45 most significant SNPs, 15 were predicted to be involved in post translational modifications. The p.Gly165Arg may interfere in homodimer interface formation. The p.Asn439Lys and p.Asp445Asn may interfere in binding interactions of MTHFD1 protein with cesium cation and potassium. The two SNPs (p.Asp562Gly and p.Gly637Cys) might interfere in interactions of MTHFD1 with ligand.     

基于Eclipse平台的本体图形编辑器的设计与实现

文章通过对Eclipse平台和GEF框架的介绍,分析了Eclipse平台在图形编辑方面的优越性.结合本体编辑,设计并实现了基于Eclipse平台下的本体编辑器,对编辑器的系统结构做了详细地分析.编辑器由模型、视图、控制以及文件四大部分组成,由于采用基于MVC模式的GEF框架,使得软件结构清晰,明显缩短了软件的开发周期,提高了软件的可维护性和代码的重用性.     

Engineering Polymer Informatics: Towards the Computer‐Aided Design of Polymers

The computer‐aided design of polymers is one of the holy grails of modern chemical informatics and of significant interest for a number of communities in polymer science. This paper outlines a vision for the in silico design of polymers and presents an information model based on modern semantic web technologies, thus laying the foundations for achieving the vision.

     

Predicting protein subcellular localization based on information content of gene ontology terms

Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.     

Computational representations of architectural design for tall buildings

Building design process is a significantly complex procedure taking into account many different factors and variables, such as the site context, environment, material availability, cost, and function. One of the most complex forms in the built environment is tall building because of the scale, design considerations, and multidisciplinary nature. This article discusses development of ontological model for understanding, presenting, relating, and managing knowledge influencing architectural design of tall buildings. Ontology is a knowledge‐based model that represents certain domain by abstraction of concepts, and a network of relationships and properties describing these concepts. By creating an architectural ontology, the factors, relations, and characteristics in the design process can be clearly defined and presented. The model incorporates physical systems such as structure, building elements, and geometry, as well as environmental effects, social aspects and other complex attributes. © 2009 Wiley Periodicals, Inc. Complexity, 2009