A DATA MANAGER for the health information system Berlin.

The needs for permanently changing the logical and physical structure of a medical datebase during the development of a health information system have initiated the project of implementing a DATA MANAGER. The concept of the DATA MANAGER covers facilities for the development of the logical data structure model including documentation of the model and programming support for application programs accessing the health information system (HIS) database. The outstanding facilities of the INTERLISP system have been found to be appropriate for writing the DATA MANAGER. A first data structure model, on which the DATA MANAGER will operate, is roughly outlined.     

A database system for the analysis of biochemical pathways

To provide support for the analysis of biochemical pathways a database system based on a model that represents the characteristics of the domain is needed. This domain has proven to be difficult to model by using conventional data modelling techniques. We are building an ontology for biochemical pathways, which acts as the basis for the generation of a database on the same domain, allowing the definition of complex queries and complex data representation. The ontology is used as a modelling and analysis tool which allows the expression of complex semantics based on a first-order logic representation language. The induction capabilities of the system can help the scientist in formulating and testing research hypotheses that are difficult to express with the standard relational database mechanisms. An ontology representing the shared formalisation of the knowledge in a scientific domain can also be used as data integration tool clarifying the mapping of concepts to the developers of different databases. In this paper we describe the general structure of our system, concentrating on the ontology-based database as the key component of the system.     

A deductive database system PACADE for analyzing 3-D and secondary structures of protein

We have developed a deductive database system PACADE for analyzing3-D and secondary structures of protein. The PA CADE systemconsists of a relational database created from Protein DataBank and a deductive engine DEE based on logic programming.It has the following features: (1) The system has an inferencemechanism. This means by which users can easily write and checkbiological hypotheses using logical and declarative rules insteadof procedural programs. (2) The relational database of the PACADE system stores data on bath 3-D and secondwy structuresof protein. The integration of this two level structure makesfeasible an abstract representation of the protein structure.We describe herein the design, functions, and implementationof this PACADE system.     

基于B /S构架医疗新技术管理系统的研发

为了使医疗新技术工作规范化、科学化,整合现有资源,进而推动医疗新技术管理工作的健康、可持续发展,设计开发了基于B/S构架的医疗新技术管理系统。采用基于B/S结构的C#编程语言、HTML语言、JavaScript 语言、AJAX脚本技术和MS SQL SERVER 2005数据库技术对系统进行实现。     

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.     

Artificial intelligence-guided analysis of cytologic data

A design for the integration of artificial intelligence (AI) technology with large databases of clinical and objective cytologic data, such as are on file at the University of Chicago, is presented. Among the key features of this approach are the use of a knowledge representation structure based upon an associative network, the use of a Bayesian belief network as a method of managing uncertainty in the system, and the use of neural networks and unsupervised learning algorithms as a means of discovering patterns within this database. Such an automated approach is necessary, given the complexity and interdependence of these data, to gain an understanding of their dependence structure and to assist in their exploration and analysis.     

Live sequence charts to model medical information

ABSTRACT: BACKGROUND: Medical records accumulate data concerning patient health and the natural history of disease progression. However, methods to mine information systematically in a form other than an electronic health record are not yet available. The purpose of this study was to develop an object modeling technique as a first step towards a formal database of medical records. METHOD: Live Sequence Charts (LSC) were used to formalize the narrative text obtained during a patient interview. LSCs utilize a visual scenario-based programming language to build object models. LSC extends the classical language of UML message sequence charts (MSC), predominantly through addition of modalities and providing executable semantics. Interobject scenarios were defined to specify natural history event interactions and different scenarios in the narrative text. Result A simulated medical record was specified into LSC formalism by translating the text into an object model that comprised a set of entities and events. The entities described the participating components (i.e., doctor, patient and record) and the events described the interactions between elements. A conceptual model is presented to illustrate the approach. An object model was generated from data extracted from an actual new patient interview, where the individual was eventually diagnosed as suffering from Chronic Fatigue Syndrome (CFS). This yielded a preliminary formal designated vocabulary for CFS development that provided a basis for future formalism of these records. CONCLUSIONS: Translation of medical records into object models created the basis for a formal database of the patient narrative that temporally depicts the events preceding disease, the diagnosis and treatment approach. The LSCs object model of the medical narrative provided an intuitive, visual representation of the natural history of the patient's disease.     

Nested Containment List (NCList): a new algorithm for accelerating interval query of genome alignment and interval databases

MOTIVATION: The exponential growth of sequence databases poses a major challenge to bioinformatics tools for querying alignment and annotation databases. There is a pressing need for methods for finding overlapping sequence intervals that are highly scalable to database size, query interval size, result size and construction/updating of the interval database. RESULTS: We have developed a new interval database representation, the Nested Containment List (NCList), whose query time is O(n + log N), where N is the database size and n is the size of the result set. In all cases tested, this query algorithm is 5-500-fold faster than other indexing methods tested in this study, such as MySQL multi-column indexing, MySQL binning and R-Tree indexing. We provide performance comparisons both in simulated datasets and real-world genome alignment databases, across a wide range of database sizes and query interval widths. We also present an in-place NCList construction algorithm that yields database construction times that are approximately 100-fold faster than other methods available. The NCList data structure appears to provide a useful foundation for highly scalable interval database applications. AVAILABILITY: NCList data structure is part of Pygr, a bioinformatics graph database library, available at http://sourceforge.net/projects/pygr     

Artificial neural networks and their use in quantitative pathology

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.     

Generalized protein tertiary structure recognition using associative memory Hamiltonians.

In previous papers, a method of protein tertiary structure recognition was described based on the construction of an associative memory Hamiltonian, which encoded the amino acid sequence and the C alpha co-ordinates of a set of database proteins. Using molecular dynamics with simulated annealing, the ability of the Hamiltonian to successfully recall the structure of a protein in the memory database was successfully demonstrated, as long as the total number of database proteins did not exceed a characteristic value, called the capacity of the Hamiltonian, equal to 0.5N to 0.7N, where N is the number of amino acid residues in the protein to be recalled. In this paper, we describe the development of additional methods to increase the capacity of the Hamiltonian, including use of a more complete representation of the protein backbone and the incorporation of contextual information into the Hamiltonian through the use of secondary structure prediction. In addition, we further extend the ability of associative memory models to predict the tertiary structures of proteins not present in the protein data set, by making the Hamiltonian invariant with respect to biological symmetries that represent site mutations and insertions and deletions. The ability of the Hamiltonian to generalize from homologous proteins to an unknown protein in the presence of other unrelated proteins in the data set is demonstrated.     

MDMPR:小鼠发育代谢表型库

小鼠发育代谢表型库(Mouse Developmental and Metabolic Phenotype Repository,MDMPR)是一个致力于小鼠资源和表型数据实时共享的开放性平台,它依托于科技部重点研发计划“发育编程及其代谢调节”专项项目“建立小鼠发育代谢表型库”。该项目预计在5年内完成500个发育代谢相关小鼠敲除模型的建立,并对其表型数据进行标准化的解析、建立表型数据库。MDMPR作为一个资源及数据集成的库,由多个子系统作为支撑,包括ES细胞数据库、项目管理系统、繁育管理系统、精子库管理系统、表型分析系统,信息化管理深入到项目中每个环节,从基因突变ES细胞制备、基因突变小鼠制备、小鼠繁育,精子冻存到最终的表型分析、数据处理及展示,保证了MDMPR产生数据的真实性及实时性。MDMPR除了不断地推进项目进行,增加自身产生的数据外,也在积极的整合其他的资源及数据,如人特异性基因敲除ES细胞库、蛋白相互作用数据库(STRING)、核心转录调节环路(dbCoRc)和Enhancer-Indel数据库,今后还将进一步整合,帮助发育代谢及其他领域的研究人员能够一站式的获取所需资源和数据、加快研究进程,最终服务于全人类的医疗事业。     

ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS ('A database for medical information systems'). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the 'simplified medical query language' (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.     

医疗质量与安全信息化监管系统架构研究

随着医疗信息技术的发展和使用,运用信息化手段开展医疗质量与安全精准化监管正在被越来越多的医院管理者所重视和接受。文章从组织保障体系建设、总体框架设计、基础数据库的标准化、主要监管内容筛选及电子病历系统等方面对医疗质量与安全信息化监管系统总体建设机构进行研究和讨论,期望能够指导医院充分利用和发挥医疗信息技术的优势,建立医疗质量与安全信息化监管系统,为制定质量管理持续改进的目标与评价改进的效果提供依据。     

SuperDrug: a conformational drug database

MOTIVATION: Different resources exist for experimentally determined and computed three-dimensional (3D)-structures of low molecular weight structures but for approved drugs, no free, publicly accessible source of 3D-structures and conformers is available. Furthermore, for selection purposes or for correlation of structural similarity with medical application, the assignment of the Anatomical Therapeutic Chemical (ATC) classification codes to each structure according to the WHO-scheme would be desirable. RESULTS: The database contains approximately 2500 3D-structures of active ingredients of essential marketed drugs. To account for structural flexibility they are represented by 10(5) structural conformers. Here we present a web-query system enabling searches for drug name, synonyms, trade name, trivial name, formula, CAS-number, ATC-code etc. 2D-similarity screening (Tanimoto coefficients) and an automatic 3D-superposition procedure based on conformational representation are implemented. Drug structures above a similarity threshold as well as superimposed conformers can be retrieved in the mol- file format via a graphical interface. AVAILABILITY: For academic use the system is accessible at http://bioinf.charite.de/superdrug. The retrieval system requires the free browser-plugin 'chime' from MDL for visualization.     

Improvements to the GDB Human Genome Data Base.

Version 6.0 of the Human Genome Data Base introduces a number of significant improvements over previous releases of GDB. The most important of these are revised data representations for genes and genomic maps and a new curatorial model for the database. GDB 6.0 is the first major genomic database to provide read/write access directly to the scientific community, including capabilities for third-party annotation. The revised database can represent all major categories of genetic and physical maps, along with the underlying order and distance information used to construct them. The improved representation permits more sophisticated map queries to be posed and supports the graphical display of maps. In addition the new GDB has a richer model for gene information, better suited for supporting cross-references to databases describing gene function, structure, products, expression and associated phenotypes.     

PFDB: a generic protein family database integrating the CATH domain structure database with sequence based protein family resources

MOTIVATION: The PFDB (Protein Family Database) is a new database designed to integrate protein family-related data with relevant functional and genomic data. It currently manages biological data for three projects-the CATH protein domain database (Orengo et al., 1997; Pearl et al., 2001), the VIDA virus domains database (Albà et al., 2001) and the Gene3D database (Buchan et al., 2001). The PFDB has been designed to accommodate protein families identified by a variety of sequence based or structure based protocols and provides a generic resource for biological research by enabling mapping between different protein families and diverse biochemical and genetic data, including complete genomes. RESULTS: A characteristic feature of the PFDB is that it has a number of meta-level entities (for example aggregation, collection and inclusion) represented as base tables in the final design. The explicit representation of relationships at the meta-level has a number of advantages, including flexibility-both in terms of the range of queries that can be formulated and the ability to integrate new biological entities within the existing design. A potential drawback with this approach-poor performance caused by the number of joins across meta-level tables-is avoided by implementing the PFDB with materialized views using the mature relational database technology of Oracle 8i. The resultant database is both fast and flexible. This paper presents the principles on which the database has been designed and implemented, and describes the current status of the database and query facilities supported.     

基于二叉分类推理的昆虫分类辅助鉴定多媒体专家系统通用平台TaxoKeys的设计与开发

本文详细介绍了基于二叉式分类推理的昆虫分类辅助鉴定多媒体专家系统通用平台TaxoKeys的设计与开发,及其所具备的主要特点。该研究根据昆虫分类学的特点,将昆虫分类的两项式检索表用数据库表示成系统知识库,利用计算机数据结构中二叉树结构的分枝结点搜索技术来实现其推理过程,进行昆虫分类的辅助鉴定,为昆虫分类专家提供一个通用专家系统平台。该系统具有可扩充性好、设计简单、操作方便等特点,同时也适用于一般性生物分类鉴定。另外,本文还就本系统功能的进一步扩展与应用研究等进行了探讨。