1306068
Parallel and distributed scientific and engineering computing
The scientific and engineering computing domains play a key role in shaping future research and development activities in academia and industry. In the not too distant future, every researcher in science and engineering fields will have to understand parallel and distributed computing. With hyperthreading in Intel processors, hypertransport links in next generation AMD processors, multicore silicon in today’s high-end microprocessors from IBM, emerging cluster and grid computing, parallel (and distributed) computing have moved into the mainstream of computing. To fully exploit these advances, researchers must start to write parallel or distributed scientific and engineering software and algorithms to cope with large and complex problems with very tight timing schedules. We would like to use this special issue to report the recent important advances in the area of parallel and distributed scientific and engineering computing. There were 33 paper submissions, not only from the Asian Pacific, but also from Europe and North America. All submissions were reviewed by at least three reviewers on relevance and technical contents on basis of papers. It was extremely difficult to select the presentation in the special issue because there were many excellent and interesting submissions. In order to allocate as many papers as possible and keep the high quality of the special issue, we finally decided to accept 13 papers. We believe all of these papers and topics will not only provide novel ideas, new results, work in progress and state-of-the-art techniques in this field, but also stimulate the future research activities in the area of parallel and distributed computing for science and engineering applications. This special issue is mainly divided into three sections, namely programming and system support, advanced numerical computation and high performance applications, respectively.
22935196
Coding and its applications in CDMA wireless systems
27711545
GenericBioMatch: A novel generic pattern match algorithm for biological sequences
GenericBioMatch is a novel algorithm for exact match in biological sequences. It allows the sequence motif pattern to contain one or more wild card letters (eg. Y, R, W in DNA sequences) and one or more gaps of any number of bases. GenericBioMatch is a relatively fast algorithm as compared to probabilistic algorithms, and has very little computational overhead. It is able to perform exact match of protein motifs as well as DNA motifs. This algorithm can serve as a quick validation tool for implementation of other algorithms, and can also serve as a supporting tool for probabilistic algorithms in order to reduce computational overhead. This algorithm has been implemented in the BioMiner software (http://iit-iti.nrc-cnrc.gc.ca/biomine e.trx), a suite of Java tools for integrated data mining in genomics. It has been tested successfully with DNA sequences from human, yeast, and Arabidopsis.
14702331
Discovery of Functional Genes for Systemic Acquired Resistance in arabidopsis Thaliana through Integrated Data Mining
Various data mining techniques combined with sequence motif information in the promoter region of genes were applied to discover functional genes that are involved in the defense mechanism of systemic acquired resistance (SAR) in Arabidopsis thaliana. A series of K-Means clustering with difference-in-shape as distance measure was initially applied. A stability measure was used to validate this clustering process. A decision tree algorithm with the discover-and-mask technique was used to identify a group of most informative genes. Appearance and abundance of various transcription factor binding sites in the promoter region of the genes were studied. Through the combination of these techniques, we were able to identify 24 candidate genes involved in the SAR defense mechanism. The candidate genes fell into 2 highly resolved categories, each category showing significantly unique profiles of regulatory elements in their promoter regions. This study demonstrates the strength of such integration methods and suggests a broader application of this approach.