14782466
Similar_Join: extending DBMS with a bio-specific operator
Existing sequence comparison software applications lack adequate automation, abstraction, performance, and flexibility. Users need a new way of studying and applying sequence comparisons in the post-genomics era. We invented and developed a new bio-specific Database Management System (DBMS) operator, Similar_Join, to abstract the labor-intensive batch sequence similarity search task into a syntactically concise database operation. We implemented the Similar_Join operator as part of a relational operator package. This implementation enabled us to write simple PL/SQL scripts within the DBMS to accomplish routine sequence similarity searches conveniently, for example, a "batch BLAST" that compares 7,000 human genes against 500,000 human Expressed Sequence Tags (EST) in a few hours. We also implemented a simple version of Similar_Join as a database operator in the extended data cartridge of Oracle 8i object-relational DBMS. When fully integrated into SQL language extensions, we demonstrated this operator could enable biology users to achieve interesting complex biological queries previously impossible inside the DBMS.
39719071
Genomic data modeling
Researchers face many challenges in representing biological data, including: (1) inherent complexity of biological data, (2) domain knowledge barrier, (3) constantly evolving knowledge, and (4) lack of expert data-modeling skills. We have studied how to represent biological sequences and sequence-related genomics concepts using logical data structure. From our multiple experiences in genomic data modeling, we present results in three areas: genomic schema elements, genomic schema fragments, and genomic data modeling lessons. A genomic schema element is a data model that contains only one basic biological sequence notion. Genomic schema elements provide biology data modelers with baseline thoughts in genomic data modeling. A genomic schema fragment is a data model that contains only one genomic topic area. Genomic schema fragments provide biology data modelers with successful design solutions that they can adapt to fit their own problem's needs. Genomic data modeling lessons address issues particularly important to genomic data modeling such as modeling contextual information, modeling intermediate and derived data, modeling inconsistent data, and modeling categorical rules. Genomic data modeling lessons provide novice biology data modelers with enriched principles from content-neutral data modeling techniques. In all, we have demonstrated how to manage evolving genomic knowledge concepts and discovery results using data modeling techniques extended into the genomics domain.
12280706
A distributed multi-agent system for collaborative information management and sharing
In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents.
10823490
Specification-based Testing for Gui-based Applications
The development of GUI-based applications has raised a lot of new issues, one of them being how to automate effective testing for applications with complicated graphical user interactions. In this paper, we discuss the architectural issues and the implementation concerns of our approach to an automated specification-based testing technique for GUI-based applications. This approach is carried out by enriching existing architecture for automated specification-based testing. An essential part of our work is a visual environment to obtain test specifications. This environment pre-runs the Application Under Test (AUT) under its own control, with two prominent characteristics: First, testers can edit test specifications within the true GUI environment of the AUT. Second, the recorded input and output contain the same references as those in the AUT, so that the test cases generated from the edited specification can be used directly by test oracles during the automated testing procedure.We present our running prototype of a visual specification editor that allows users to graphically manipulate test specifications when these specifications are given in term of Finite State Machines (FSM) and the implementations of the AUT are GUI-based Java applications.
8408042
The Rat Genome Database (RGD): developments towards a phenome database
The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms.
1954824
ChemDB: a public database of small molecules and related chemoinformatics resources
MOTIVATION
The development of chemoinformatics has been hampered by the lack of large, publicly available, comprehensive repositories of molecules, in particular of small molecules. Small molecules play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes in chemical genomics and systems biology, and for the screening and discovery of new drugs and other useful compounds.


RESULTS
We describe ChemDB, a public database of small molecules available on the Web. ChemDB is built using the digital catalogs of over a hundred vendors and other public sources and is annotated with information derived from these sources as well as from computational methods, such as predicted solubility and three-dimensional structure. It supports multiple molecular formats and is periodically updated, automatically whenever possible. The current version of the database contains approximately 4.1 million commercially available compounds and 8.2 million counting isomers. The database includes a user-friendly graphical interface, chemical reactions capabilities, as well as unique search capabilities.


AVAILABILITY
Database and datasets are available on http://cdb.ics.uci.edu.
7817604
Topological-based classification using artificial gene networks
The topological properties of artificial gene networks that rely on the assessment of similarities between the expressions profile of gene pairs would be consistent with specific cellular states. Furthermore, some topological properties of the network would have significant variance regarding different cellular states. In this study, we proposed a novel and highly efficient computational framework for a topological-based classification using microarray gene expression data. Owing to the high prediction accuracy with this approach, we believe that there is a noticeable advantage of using topological-based classification. We used the microarray gene expression data sets containing 14 classes of cancer to construct 14 basic artificial gene networks accordingly. For each test sample, we add the sample into the data set of each class and reconstruct all of the networks. Cancer type was classified according to the correlation of topological quantity between the basic artificial gene networks and the reconstructed networks. Total classification accuracy can achieve 78.26% in the test data set (95.83% in the standard data set). After screening the quality of the standard data set, we can achieve a prediction accuracy of 86.48% in the test data set. Thus, we can achieve higher prediction accuracy in comparison to previous studies.