5414394
Artificial Neural Networks for Molecular Sequence Analysis
Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.
7795322
The Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.
14776207
BioThesaurus: a web-based thesaurus of protein and gene names
UNLABELLED
BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to protein entries in the UniProt Knowledgebase. Currently covering more than two million proteins, BioThesaurus consists of over 2.8 million names extracted from multiple molecular biological databases according to the database cross-references in iProClass. The BioThesaurus web site allows the retrieval of synonymous names of given protein entries and the identification of protein entries sharing the same names.


AVAILABILITY
BioThesaurus is accessible for online searching at http://pir.georgetown.edu/iprolink/biothesaurus
21872273
The Protein Information Resource (PIR)
The Protein Information Resource (PIR) produces the largest, most comprehensive, annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Sequence Database (JIPID). The expanded PIR WWW site allows sequence similarity and text searching of the Protein Sequence Database and auxiliary databases. Several new web-based search engines combine searches of sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. New capabilities for searching the PIR sequence databases include annotation-sorted search, domain search, combined global and domain search, and interactive text searches. The PIR-International databases and search tools are accessible on the PIR WWW site at http://pir.georgetown.edu and at the MIPS WWW site at http://www. mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.
471313
Dependence network modeling for biomarker identification
MOTIVATION
Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.


RESULTS
Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.


AVAILABILITY
See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/
205231250
The Universal Protein Resource (UniProt)
The ability to store and interconnect all available information on proteins is crucial to modern biological research. Accordingly, the Universal Protein Resource (UniProt) plays an increasingly important role by providing a stable, comprehensive, freely accessible central resource on protein sequences and functional annotation. UniProt is produced by the UniProt Consortium, formed in 2002 by the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt web site and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of three major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase and the UniProt Reference Clusters. An additional component consisting of metagenomic and environmental sequences has recently been added to UniProt to ensure availability of such sequences in a timely fashion. UniProt is updated and distributed on a bi-weekly basis and can be accessed online for searches or download at .
27585937
Multi-modal Sign Icon Retrieval for Augmentative Communication
This paper addresses a multi-modal sign icon retrieval and prediction technology for generating sentences from ill-formed Taiwanese sign language (TSL) for people with speech or hearing impairments. The design and development of this PC-based TSL augmented and alternative communication (AAC) system aims to improve the input rate and accuracy of communication aids. This study focuses on 1) developing a effective TSL icon retrieval method, 2) investigating TSL prediction strategies for input rate enhancement, 3) using a predictive sentence template (PST) tree for sentence generation. The proposed system assists people with language disabilities in sentence formation. To evaluate the performance of our approach, a pilot study for clinical evaluation and education training was undertaken. The evaluation results show that the retrieval rate and subjective satistactory level for sentence generation was significantly improved.
3236118
Human performance modeling in temporary segmentation Chinese character handwriting recognizers
Human performance in Chinese character handwriting recognizers is critical to the satisfaction and acceptance of their users. Based on Teal's [CHI'92 (1992) p. 295] interactive model, a static model describing the independent factors in determining the task completion time was set up with a simple mathematical inference; in addition, a dynamic model describing these factors' direct and indirect causal relationship was established by the path analytic method. Results in Experiment 1 indicated that both the static model and the dynamic model could fit observed task completion time satisfactorily with minor modifications. In addition, with users' average writing time around 1500 ms for each frequently used character, it was found that the user's performance was impaired significantly when segmentation time was longer than 1040 ms. An integrated model was devised after combining the static and dynamic models. Experiment 2 testified the integrated model in another handwriting recognizer and found that it could still fit human performance data with users in three different training conditions. Implications of the integrated model are that: (1) when recognition accuracy and number of inputting characters are constant, the weights of average writing time for each character, segmentation time, recognition time in determining task completion time are equal but bigger than the weight of the repairing time; (2) when the repairing time, average writing time for each character, segmentation time and recognition time are constant, there is an inverse model between task completion time and recognition accuracy; when recognition accuracy is from 50% to 93%, every 1% increase of recognition accuracy will reduce task completion time from 1989 to 1915 ms; when recognition accuracy increases from 94% to 100%, every 1% increase of recognition accuracy will reduce task completion time from 1895 to 1392 ms. Guidelines in designing these recognizers were given based on these implications.
5461786
Analysis and Comparison of Geometric and Algebraic Multigrid for Convection-Diffusion Equations
The discrete convection-diffusion equations obtained from streamline diffusion finite element discretization are solved on both uniform meshes and adaptive meshes. Estimates of error reduction rates for both geometric multigrid (GMG) and algebraic multigrid (AMG) are established on uniform rectangular meshes for a model problem. Our analysis shows that GMG with line Gauss-Seidel smoothing and bilinear interpolation converges if $h\gg \epsilon^{2/3}$, and AMG with the same smoother converges more rapidly than GMG if the interpolation constant $\beta$ in the approximation assumption of AMG satisfies $\beta \ll (\frac{h}{\sqrt{\epsilon}})^{\alpha}, \hs{1mm} \mbox{where $ \alpha = \Big\{ {\begin{smallmatrix} 1, & {h < \sqrt \varepsilon, } \\ 2, & {h \ge \sqrt \varepsilon.} \\ \end{smallmatrix}}$}$ On unstructured triangular meshes, the performance of GMG and AMG, both as solvers and as preconditioners for GMRES, are evaluated. Numerical results show that GMRES with AMG preconditioning is a robust and reliable solver on both type of meshes.
15660415
On-the-fly sharing for streamed aggregation
Data streaming systems are becoming essential for monitoring applications such as financial analysis and network intrusion detection. These systems often have to process many similar but different queries over common data. Since executing each query separately can lead to significant scalability and performance problems, it is vital to share resources by exploiting similarities in the queries. In this paper we present ways to efficiently share streaming aggregate queries with differing periodic windows and arbitrary selection predicates. A major contribution is our sharing technique that does not require any up-front multiple query optimization. This is a significant departure from existing techniques that rely on complex static analyses of fixed query workloads. Our approach is particularly vital in streaming systems where queries can join and leave the system at any point. We present a detailed performance study that evaluates our strategies with an implementation and real data. In these experiments, our approach gives us as much as an order of magnitude performance improvement over the state of the art.
490124
Modeling Psychological Refractory Period (PRP) and Practice Effect on PRP with Queuing Networks and Reinforcement Learning Algorithms
PRP (Psychological Refractory Period) is a basic but important form of human information processing in dual-task situations. This article describes a queuing network model of PRP that successfully modeled PRP without the need of setting up complex lock/unlock performance strategies employed in the EPIC model of PRP or drawing complex scheduling charts employed in the ACT-R/PM model of PRP. Further, by integrating queuing networks with reinforcement learning algorithms, the queuing network model successfully simulated practice effect on PRP, which has not been modeled in existing PRP models. The current research indicates that depending on an individual’s degree of practice, cognition can be either serial or parallel at the level of production or response selection. Extensions of queuing network model in modeling other tasks and its easiness in modeling concurrent tasks in practice are also discussed.