MDSM: Microarray database schema matching using the Hungarian method

Current microarray databases use different terminologies and structures and thereby limit the sharing of data and collating of results between laboratories. Consequently, an effective integrated microarray data model is required. One important process to develop such an integrated database is schema matching. In this paper, we propose an effective schema matching approach called MDSM, to syntactically and semantically map attributes of different microarray schemas. The contribution from this work will be used later to create microarray global schemas. Since microarray data is complex, we use microarray ontology to improve the measuring accuracy of the similarity between attributes. The similarity relations can be represented as weighted bipartite graphs. We determine the best schema matching by computing the optimal matching in a bipartite graph using the Hungarian optimisation method. Experimental results show that our schema matching approach is effective and flexible to use in different kinds of database models such as; database schema, XML schema, and web site map. Finally, a case study on an existing public microarray schema is carried out using the proposed method.     

基因芯片技术分析20 cGy γ射线照射人淋巴母细胞中差异表达的基因谱

应用基因芯片技术对20 cGy ^60Coγ射线照射的人淋巴母细胞和对照细胞中的mRNA表达水平进行对比杂交分析，探索差异表达基因。结果发现在所分析的14112条日的基因中差异表达显著的基因（2倍以上差异）有83条，其中表达上调的有21条，表达下降的有62条，这些基因表达产物涉及到信号转导、细胞周期调节、免疫相关蛋白、细胞骨架与运动等功能基因。表明低剂量辐射对多种功能基因表达产生了影响，是调控细胞辐射反应最基本的分子基础。     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.     

Extracting gene regulation information for cancer classification

In this paper, we address the problem of extracting gene regulation information from microarray data for cancer classification. From the biological viewpoint, a model of gene regulation probability is established where three types of gene regulation states in a tissue sample are assumed and then two regulation events correlated with the class distinction are defined. Different from the previous approaches, the proposed algorithm uses gene regulation probabilities as carriers of regulation information to select genes and construct classifiers. The proposed approach is successfully applied to two public available microarray data sets, the leukemia data and the prostate data. Experimental results suggest that gene selection based on regulation information can greatly improve cancer classification, and the classifier based on regulation information is more efficient and more stable than several previous classification algorithms.     

滚环DNA扩增技术及其应用

滚环扩增技术是一种等温信号扩增方法,DNA可在很短时间内实现指数扩增,因此,可用于痕量分子的检测.目前,该技术既可以扩增环状DNA、RNA,也可以扩增线性DNA,甚至全基因组DNA.目前,该技术主要用于全基因组扩增、核酸测序、单核苷酸多态性以及DNA芯片、蛋白质芯片分析等广泛领域.     

Improvement of the durability of porous silicon through functionalisation for biomedical applications

The durability of porous silicon (PS) in solutions was improved by grafting a molecule, 2,4,6,8-tetramethyl-2,4,6,8-tetravinyl-1,3,5,7,2,4,6,8-tetraoxatetrasilocane (TE), with four terminal vinyl groups. With a native PS sample as control, we compared the long-term durability of three modified PS samples: TE-, undec-10-enoic acid (UA)-, and TE/UA(TE first and UA followed)-grafted PS, in a weak organic base of dimethyl sulfoxide, an aqueous mineral solution of CuBr₂, and phosphate buffered saline respectively. Results indicate that TE-grafting is a straightforward and impactful approach to protect PS from oxidation and degradation. Further we used the TE-grafted PS to fabricate a prototype protein microarray by post-grafting UA and subsequently converting UA to nitrilotriacetic acid/Ni²⁺ for binding histidine-tagged proteins.     

Monte Carlo randomization tests for large-scale abundance datasets on the GPU

Statistical tests are often performed to discover which experimental variables are reacting to specific treatments. Time-series statistical models usually require the researcher to make assumptions with respect to the distribution of measured responses which may not hold. Randomization tests can be applied to data in order to generate null distributions non-parametrically. However, large numbers of randomizations are required for the precise p-values needed to control false discovery rates. When testing tens of thousands of variables (genes, chemical compounds, or otherwise), significant q-value cutoffs can be extremely small (on the order of 10⁻⁵ to 10⁻⁸). This requires high-precision p-values, which in turn require large numbers of randomizations. The NVIDIA^® Compute Unified Device Architecture^® (CUDA^®) platform for General Programming on the Graphics Processing Unit (GPGPU) was used to implement an application which performs high-precision randomization tests via Monte Carlo sampling for quickly screening custom test statistics for experiments with large numbers of variables, such as microarrays, Next-Generation sequencing read counts, chromatographical signals, or other abundance measurements. The software has been shown to achieve up to more than 12 fold speedup on a Graphics Processing Unit (GPU) when compared to a powerful Central Processing Unit (CPU). The main limitation is concurrent random access of shared memory on the GPU. The software is available from the authors.     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Antibody-based computing: an application to the stable marriage problem

Antibodies, among other things, are important components of the immune system. This paper proposes using the specific recognition capability exhibited by antibodies for computation, in particular, for solving the stable marriage problem, which has been studied as a combinatorial computational problem. Antibody-based computation is proposed by integrating the recognition capabilities of antibodies. The computation is carried out on an array form that is suitable not only for expressing stable marriage problems, but also for further integration to antibody microarrays. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007