保存基因的2-Opt一般反向差分演化算法

为了进一步提高差分演化算法的性能,提出一种采用保存基因的2-Opt一般反向差分演化算法,并把它应用于函数优化问题中.新算法具有以下特征:(1)采用保存被选择个体基因的方式组成参加演化的新个体.保存基因的方法可以很好的保持种群多样性;(2)采用一般反向学习(GOBL)机制进行初始化,提高了初始化效率;(3)采用2-Opt算法加速差分演化算法的收敛速度,提高搜索效率.通过测试函数的实验,并与其他差分演化算法进行比较.实验结果证实了新算法的高效性,通用性和稳健性.     

真核生物多基因预测结果整合算法

针对独立基因预测算法可靠性较差的缺点,提出了真核生物多基因预测结果整合算法(Algorithm for integration of multiple eukaryotie gene prediction results,AIMEGPR).该算法在综合分析各种预测算法结果的基础上,首先用极大似然法估计各种预测算法的性能参数,然后利用这些性能参数计算基因证据区间上各个基因片段归属于各种基因元件类型的后验概率,最后采用动态规划法在基因证据区间上确定最优的一致基因结构.AIMEGPR既不需要人工定制整合规则,也不需要复杂的训练学习,因此AIMEGPR尤其在基因组新测序物种上进行编码基因注释时具有十分显著的优越性.实验结果表明,利用AIMEGPR算法对多基因预测结果整合可以明显提高一致基因的可靠性.     

利用缓存优化关系数据的XML发布

随着Web的不断发展,XML逐渐成为Web数据表示和交换的标准．但是大量企业数据仍然存储在关系数据库中,因此必须将关系数据发布成为XML文档并且传送给合作者．目前广泛采用的发布方式是针对每个用户的请求独立完成的．但是这样的发布方法忽视了用户发布请求所具有的相似性,导致发布成本高和响应时间长的问题．基于用户发布请求的相似性,提出了挖掘频繁发布请求并且缓存中间结果的解决方法．当新的发布请求发出时就可以利用缓存的中间结果,从而在很大程度上降低响应时间．初步实验研究表明提出的方法具有较高的效率．     

A Novel Approach to Revealing Positive and Negative Co-Regulated Genes

As explored by biologists, there is a real and emerging need to identify co-regulated gene clusters, which include both positive and negative regulated gene clusters. However, the existing pattern-based and tendency-based clustering approaches are only designed for finding positive regulated gene clusters. In this paper, a new subspace clustering model called g-Cluster is proposed for gene expression data. The proposed model has the following advantages： 1） find both positive and negative co-regulated genes in a shot, 2） get away from the restriction of magnitude transformation relationship among co-regulated genes, and 3） guarantee quality of clusters and significance of regulations using a novel similarity measurement gCode and a user-specified regulation threshold δ, respectively. No previous work measures up to the task which has been set. Moreover, MDL technique is introduced to avoid insignificant g-Clusters generated. A tree structure, namely GS-tree, is also designed, and two algorithms combined with efficient pruning and optimization strategies to identify all qualified g-Clusters. Extensive experiments are conducted on real and synthetic datasets. The experimental results show that 1） the algorithm is able to find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance, and 2） the algorithms are effective and efficient, and outperform the existing approaches.     

Yeast exo-β-glucanases can be used as efficient and readily detectable reporter genes in Saccharomyces cerevisiae

Yeast exo-1,3-β-glucanases are secretable proteins whose function is basically trophic and may also be involved in cell wall glucan hydrolytic processes. Since fluorescein di(β-D -glucopyranoside) is a fluorogenic substrate detectable and quantifiable by flow cytometry, it was used for testing the ability of the EXG1 gene product of Saccharomyces cerevisiae and its homologous gene in Candida albicans to function as reporter genes. These open reading frames were coupled to different promoters in multicopy plasmids, and exoglucanase activity quantified at flow cytometry. Exoglucanases were found to be useful tools for the study of promoter regions in S. cerevisiae. This technique has the advantage over other reporter gene systems—such as β-galactosidase fusions—that it does not require permeabilization of yeast cells and therefore it allows the recovery of viable cells—by sorting—after flow cytometry analysis.     

Genetic mapping of the α-galactosidase MEL gene family on right and left telomeres of Saccharomyces cerevisiae

The α-galactosidase MEL2–MEL10 genes have been genetically mapped to right and left telomere regions of the following chromosomes of Saccharomyces cerevisiae: MEL2 at VII L, MEL3 at XVI L, MEL4 at XI L, MEL5 at IV L, MEL6 at XIII R, MEL7 at VI R, MEL8 at XV R, MEL9 at X R and MEL10 at XII R. A set of tester strains with URA3 inserted into individual telomeres and no MEL genes was used for mapping.