21970380
Counter-Propagation Neural Networks for Molecular Sequence Classification: Supervised LVQ and Dynamic Node Allocation
A modified counter-propagation (CP) algorithm with supervised learning vector quantizer (LVQ) and dynamic node allocation has been developed for rapid classification of molecular sequences. The molecular sequences were encoded into neural input vectors using an n–gram hashing method for word extraction and a singular value decomposition (SVD) method for vector compression. The neural networks used were three-layered, forward-only CP networks that performed nearest neighbor classification. Several factors affecting the CP performance were evaluated, including weight initialization, Kohonen layer dimensioning, winner selection and weight update mechanisms. The performance of the modified CP network was compared with the back-propagation (BP) neural network and the k–nearest neighbor method. The major advantages of the CP network are its training and classification speed and its capability to extract statistical properties of the input data. The combined BP and CP networks can classify nucleic acid or protein sequences with a close to 100% accuracy at a rate of about one order of magnitude faster than other currently available methods.
6603995
Protein family classification and functional annotation
With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.
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 .
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.
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.
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.
31789422
Performance Analysis of Multistage Interconnection Network Configurations and Operations
A performance evaluation using both analytical and simulation models, of circuit-switching multistage interconnection networks in aspects of configurations and operations is presented. Two configurations of the networks, single and dual, are evaluated. Network operations considered include conflict resolution strategies and communication strategies. Two different conflict resolution strategies, drop and hold, are analyzed. New analyses using Markov chains, are given, and are verified by simulation results. In the single-network configuration, it is shown that the drop strategy is better than the hold strategy for randomly distributed traffic. In the dual network configuration, five different communication strategies are investigated, and the optimum performance level is shown to be dependent on the length of the data transfer time. >
1308934
A World-Wide Web server on a multicomputer system
As the number of people browsing the world-wide web increases explosively, the workload of popular web servers also increases rapidly. A multicomputer system that was designed for I/O intensive applications has been found to be quite suitable for serving as a web server. The system is composed of multiple clusters of multiprocessors interconnected by an interconnection network. The interconnection network is an ATM-like cell-based switching network which can be restructured so that the system can be scaled up to meet the increasing demands of web service. It is also found that the multicomputer system can support video streams effectively. Design of the system as well as porting of web servers on to it are discussed in this paper.
15142715
A cokriging method for estimating population density in urban areas
Population information is typically available for analysis in aggregate socioeconomic reporting zones, such as census blocks in the United States and enumeration districts in the United Kingdom. However, such data mask underlying individual population distributions and may be incompatible with other information sources (e.g. school districts, transportation analysis zones, metropolitan statistical areas, etc.). Moreover, it is well known that there are potential significance issues associated with scale and reporting units, the modifiable areal unit problem (MAUP), when such data are used in analysis. This may lead to biased results in spatial modeling approaches. In this study, impervious surface fraction derived from Thematic Mapper (TM) imagery was applied to derive the underlying population of an urban region. A cokriging method was developed to interpolate population density by modeling the spatial correlation and cross-correlation of population and impervious surface fraction. Results suggest that population density can be accurately estimated using cokriging applied to impervious surface fraction. In particular, the relative population estimation error is � 0.3% for the entire study area and 10–15% at block group and tract levels. Moreover, unlike other interpolation methods, cokriging gives estimation variance at the TM pixel level. 2005 Elsevier Ltd. All rights reserved.
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.