As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

BACKGROUND: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. RESULTS: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. CONCLUSION: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.

This paper describes NeuroExtract, a pilot system which facilitates the integrated retrieval of Internet-based information relevant to the neurosciences. The approach involved extracting descriptive metadata from the sources using domain-specific queries; retrieving, processing, and organizing the data into structured text files; searching the data files using text-based queries; and, providing the results in a Web page along with descriptions to entries and URL links to the original sources. NeuroExtract has been implemented for three bioscience resources, SWISSPROT, GEO, and PDB, which provide neuroscience-related information as sub-topics. We discuss several issues that arose in the course of NeuroExtract's implementation. This project is a first step in exploring how this general approach might be used, in conjunction with other query mediation approaches, to facilitate the integration of many Internet-accessible resources relevant to the neurosciences.

The present study described an open source application, ResourceLog, that allows website administrators to record and analyze the usage of online resources. The application includes four components: logging, data mining, administrative interface, and back-end database. The logging component is embedded in the host website. It extracts and streamlines information about the Web visitors, the scripts, and dynamic parameters from each page request. The data mining component runs as a set of scheduled tasks that identify visitors of interest, such as those who have heavily used the resources. The identified visitors will be automatically subjected to a voluntary user survey. The usage of the website content can be monitored through the administrative interface and subjected to statistical analyses. As a pilot project, ResourceLog has been implemented in SenseLab, a Web-based neuroscience database system. ResourceLog provides a robust and useful tool to aid system evaluation of a resource-driven Web application, with a focus on determining the effectiveness of data sharing in the field and with the general public.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

In 2002-2003, the American College of Medical Informatics (ACMI) undertook a study of the future of informatics training. This project capitalized on the rapidly expanding interest in the role of computation in basic biological research, well characterized in the National Institutes of Health (NIH) Biomedical Information Science and Technology Initiative (BISTI) report. The defining activity of the project was the three-day 2002 Annual Symposium of the College. A committee, comprised of the authors of this report, subsequently carried out activities, including interviews with a broader informatics and biological sciences constituency, collation and categorization of observations, and generation of recommendations. The committee viewed biomedical informatics as an interdisciplinary field, combining basic informational and computational sciences with application domains, including health care, biological research, and education. Consequently, effective training in informatics, viewed from a national perspective, should encompass four key elements: (1). curricula that integrate experiences in the computational sciences and application domains rather than just concatenating them; (2). diversity among trainees, with individualized, interdisciplinary cross-training allowing each trainee to develop key competencies that he or she does not initially possess; (3). direct immersion in research and development activities; and (4). exposure across the wide range of basic informational and computational sciences. Informatics training programs that implement these features, irrespective of their funding sources, will meet and exceed the challenges raised by the BISTI report, and optimally prepare their trainees for careers in a field that continues to evolve.

Query Integrator System (QIS) is a database mediator framework intended to address robust data integration from continuously changing heterogeneous data sources in the biosciences. Currently in the advanced prototype stage, it is being used on a production basis to integrate data from neuroscience databases developed for the SenseLab project at Yale University with external neuroscience and genomics databases. The QIS framework uses standard technologies and is intended to be deployable by administrators with a moderate level of technological expertise: It comes with various tools, such as interfaces for the design of distributed queries. The QIS architecture is based on a set of distributed network-based servers, data source servers, integration servers, and ontology servers, that exchange metadata as well as mappings of both metadata and data elements to elements in an ontology. Metadata version difference determination coupled with decomposition of stored queries is used as the basis for partial query recovery when the schema of data sources alters.

This report describes XDesc (eXperiment Description), a pilot project that serves as a case study exploring the degree to which an informatics capability developed in a clinical application can be ported for use in the biosciences. In particular, XDesc uses the Entity-Attribute-Value database implementation (including a great deal of metadata-based functionality) developed in TrialDB, a clinical research database, for use in describing the samples used in microarray experiments stored in the Yale Microarray Database (YMD). XDesc was linked successfully to both TrialDB and YMD, and was used to describe the data in three different microarray research projects involving Drosophila. In the process, a number of new desirable capabilities were identified in the bioscience domain. These were implemented on a pilot basis in XDesc, and subsequently "folded back" into TrialDB itself, enhancing its capabilities for dealing with clinical data. This case study provides a concrete example of how informatics research and development in clinical and bioscience domains has the potential for synergy and for cross-fertilization.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create "portals" that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.

This case study describes a project that explores issues of quality of service (QoS) relevant to the next-generation Internet (NGI), using the PathMaster application in a testbed environment. PathMaster is a prototype computer system that analyzes digitized cell images from cytology specimens and compares those images against an image database, returning a ranked set of "similar" cell images from the database. To perform NGI testbed evaluations, we used a cluster of nine parallel computation workstations configured as three subclusters using Cisco routers. This architecture provides a local "simulated Internet" in which we explored the following QoS strategies: (1) first-in-first-out queuing, (2) priority queuing, (3) weighted fair queuing, (4) weighted random early detection, and (5) traffic shaping. The study describes the results of using these strategies with a distributed version of the PathMaster system in the presence of different amounts of competing network traffic and discusses certain of the issues that arise. The goal of the study is to help introduce NGI QoS issues to the Medical Informatics community and to use the PathMaster NGI testbed to illustrate concretely certain of the QoS issues that arise.

The retrieval of useful data from spotted microarray slides requires keeping track of which microplate wells and DNA sample corresponds to each spot on each array slide. Existing approaches are closely coupled with the type of arrayer in use and are computer operating-system-specific. To support the microarray researcher community at large who use different arrayers and computer platforms, increased flexibility, generality, and portability of these approaches are required. In this paper, we describe a general algorithm that correlates the well positions of DNA samples in each microplate to the positions of the spots on each array slide. Based on this algorithm, we have implemented a flexible and platform-independent program named MicroArray Convolutor (MAC) that provides a Web solution allowing the user to: (a) import a text file that identifies the DNA samples and their well locations, (b) select a transformation method that converts data in 96-well plate format into 384-well plate format, and (c) specify the output format of the array lists dependant on the configuration of the array platform as well as the downstream analysis software chosen for the array. MAC and its source code can be accessed via the following Web address: http://ymd.med.yale.edu/kei-cgi/kc_mac_dev8.pl.

As the eXtensible Markup Language (XML) becomes a popular or standard language for exchanging data over the Internet/Web, there are a growing number of genome Web sites that make their data available in XML format. Publishing genomic data in XML format alone would not be that useful if there is a lack of development of software applications that could take advantage of the XML technology to process these XML-formatted data. This paper illustrates the usefulness of XML in representing and interoperating genomic data between two different data sources (Snyder's laboratory at Yale and SGD at Stanford). In particular, we compare the locations of transposon insertions in the yeast DNA sequences that have been identified by BLAST searches with the chromosomal locations of the yeast open reading frames (ORFs) stored in SGD. Such a comparison allows us to characterize the transposon insertions by indicating whether they fall into any ORFs (which may potentially encode proteins that possess essential biological functions). To implement this XML-based interoperation, we used NCBIs "blastall" (which gives an XML output option) and SGD's yeast nucleotide sequence dataset to establish a local blast server. Also, we converted the SGD's ORF location data file (which is available in tab-delimited formal) into an XML document based on the BIOML (BIOpolymer Markup Language) standard.

The paper describes T/Gen, a prototype computer-based tool designed to help maintain the knowledge in a computer-based clinical practice guideline that provides patient-specific recommendations. T/Gen takes as input a set of clinical conditions to which a guideline must react, and allows the user to specify domain-specific constraints as to which combinations of conditions do not make sense or do not need to be exhaustively tested against one another. T/Gen automatically generates constrained sets of combinations of clinical conditions, each corresponding to a clinical case (or to several closely related clinical cases) that can be used to help test the computer-based guideline. The combinations can be used to test the guideline logic using T/Gen's built-in logic interpreter, or to generate a set of test cases for use in testing an operational guideline system. T/Gen has been developed and tested with five pilot guidelines, for two childhood immunization series, for influenza vaccination, for primary thyroid screening, and for embryo transplantation. The paper describes how T/Gen's approach is implemented for the five pilot guidelines, outlines the current status and future directions of the project, and discusses the design issues that arose in the course of carrying out the work.

The paper provides an overview of neuroinformatics research at Yale University being performed as part of the national Human Brain Project. This research is exploring the integration of multidisciplinary sensory data, using the olfactory system as a model domain. The neuroinformatics activities fall into three main areas: 1) building databases and related tools that support experimental olfactory research at Yale and can also serve as resources for the field as a whole, 2) using computer models (molecular models and neuronal models) to help understand data being collected experimentally and to help guide further laboratory experiments, 3) performing basic neuroinformatics research to develop new informatics technologies, including a flexible data model (EAV/CR, entity-attribute-value with classes and relationships) designed to facilitate the integration of diverse heterogeneous data within a single unifying framework.

BACKGROUND: The entity-attribute-value representation with classes and relationships (EAV/CR) provides a flexible and simple database schema to store heterogeneous biomedical data. In certain circumstances, however, the EAV/CR model is known to retrieve data less efficiently than conventionally based database schemas. OBJECTIVE: To perform a pilot study that systematically quantifies performance differences for database queries directed at real-world microbiology data modeled with EAV/CR and conventional representations, and to explore the relative merits of different EAV/CR query implementation strategies. METHODS: Clinical microbiology data obtained over a ten-year period were stored using both database models. Query execution times were compared for four clinically oriented attribute-centered and entity-centered queries operating under varying conditions of database size and system memory. The performance characteristics of three different EAV/CR query strategies were also examined. RESULTS: Performance was similar for entity-centered queries in the two database models. Performance in the EAV/CR model was approximately three to five times less efficient than its conventional counterpart for attribute-centered queries. The differences in query efficiency became slightly greater as database size increased, although they were reduced with the addition of system memory. The authors found that EAV/CR queries formulated using multiple, simple SQL statements executed in batch were more efficient than single, large SQL statements. CONCLUSION: This paper describes a pilot project to explore issues in and compare query performance for EAV/CR and conventional database representations. Although attribute-centered queries were less efficient in the EAV/CR model, these inefficiencies may be addressable, at least in part, by the use of more powerful hardware or more memory, or both.

OBJECTIVE: Currently, when cytopathology images are archived, they are typically stored with a limited text-based description of their content. Such a description inherently fails to quantify the properties of an image and refers to an extremely small fraction of its information content. This paper describes a method for automatically indexing images of individual cells and their associated diagnoses by computationally derived cell descriptors. This methodology may serve to better index data contained in digital image databases, thereby enabling cytologists and pathologists to cross-reference cells of unknown etiology or nature. DESIGN: The indexing method, implemented in a program called PathMaster, uses a series of computer-based feature extraction routines. Descriptors of individual cell characteristics generated by these routines are employed as indexes of cell morphology, texture, color, and spatial orientation. MEASUREMENTS: The indexing fidelity of the program was tested after populating its database with images of 152 lymphocytes/lymphoma cells captured from lymph node touch preparations stained with hematoxylin and eosin. Images of "unknown" lymphoid cells, previously unprocessed, were then submitted for feature extraction and diagnostic cross-referencing analysis. RESULTS: PathMaster listed the correct diagnosis as its first differential in 94 percent of recognition trials. In the remaining 6 percent of trials, PathMaster listed the correct diagnosis within the first three "differentials." CONCLUSION: PathMaster is a pilot cell image indexing program/search engine that creates an indexed reference of images. Use of such a reference may provide assistance in the diagnostic/prognostic process by furnishing a prioritized list of possible identifications for a cell of uncertain etiology.

This paper provides a "viewpoint discussion" based on a presentation made to the 2000 Symposium of the American College of Medical Informatics. It discusses potential opportunities for researchers in health informatics to become involved in the rapidly growing field of bioinformatics, using the activities of the Yale Center for Medical Informatics as a case study. One set of opportunities occurs where bioinformatics research itself intersects with the clinical world. Examples include the correlations between individual genetic variation with clinical risk factors, disease presentation, and differential response to treatment; and the implications of including genetic test results in the patient record, which raises clinical decision support issues as well as legal and ethical issues. A second set of opportunities occurs where bioinformatics research can benefit from the technologic expertise and approaches that informaticians have used extensively in the clinical arena. Examples include database organization and knowledge representation, data mining, and modeling and simulation. Microarray technology is discussed as a specific potential area for collaboration. Related questions concern how best to establish collaborations with bioscientists so that the interests and needs of both sets of researchers can be met in a synergistic fashion, and the most appropriate home for bioinformatics in an academic medical center.

OBJECTIVE: To query a clinical data repository (CDR) for answers to clinical questions to determine whether different types of fields (coded and free text) would yield confirmatory, complementary, or conflicting information and to discuss the issues involved in producing the discrepancies between the fields. METHODS: The appropriate data fields in a subset of a CDR (5,135 patient records) were searched for the answers to three questions related to surgical procedures. Each search included at least one coded data field and at least one free-text field. The identified free-text records were then searched manually to ensure correct interpretation. The fields were then compared to determine whether they agreed with each other, were supportive of each other, contained no entry (absence of data), or were contradictory. RESULTS: The degree of concordance varied greatly according to the field and the question asked. Some fields were not granular enough to answer the question. The free-text fields often gave an answer that was not definitive. Absence of data was most logically interpreted in some cases as lack of completion of data and in others as a negative answer. Even with a question as specific as which side a hernia was on, contradictory data were found in 5 to 8 percent of the records. CONCLUSIONS: Using the data in the CDR to answer clinical questions can yield significantly disparate results depending on the question and which data fields are searched. A database cannot just be queried in automated fashion and the results reported. Both coded and textual fields must be searched to obtain the fullest assessment. This can be expected to result in information that may be confirmatory, complementary, or conflicting. To yield the most accurate information possible, final answers to questions require human judgment and may require the gathering of additional information.

IMM/Scrub is a pilot tool developed to assist in the deduplication of vaccination history records in childhood immunization registries. This problem is complicated by a number of factors including that fact that: (1) some doses are numbered and some are not, (2) doses may have different dose numbers, (3) doses may specify different preparations within a vaccine series, (4) one dose may indicate a combination vaccine and the other dose may specify one component of that combination, (5) two doses may have slightly different dates, and (6) combinations of any of these problems may occur together. IMM/Scrub is designed to help detect 10 different types of vaccination dose duplicates and also allows the user to specify flexibly the conditions in which a duplicate dose might be automatically eliminated. In addition, IMM/Scrub is linked to the IMM/Serve immunization forecasting program, which can provide additional assistance in the data cleaning process. The paper describes (1) the design of the current pilot implementation of IMM/Scrub, (2) the lessons learned during its implementation, and (3) our preliminary experience applying it to data from three immunization databases, from a state, a metropolitan area, and an academic medical center.

Entity-attribute-value (EAV) representation is a means of organizing highly heterogeneous data using a relatively simple physical database schema. EAV representation is widely used in the medical domain, most notably in the storage of data related to clinical patient records. Its potential strengths suggest its use in other biomedical areas, in particular research databases whose schemas are complex as well as constantly changing to reflect evolving knowledge in rapidly advancing scientific domains. When deployed for such purposes, the basic EAV representation needs to be augmented significantly to handle the modeling of complex objects (classes) as well as to manage interobject relationships. The authors refer to their modification of the basic EAV paradigm as EAV/CR (EAV with classes and relationships). They describe EAV/CR representation with examples from two biomedical databases that use it.

ACT/DB is a client-server database application for storing clinical trials and outcomes data, which is currently undergoing initial pilot use. It stores most of its data in entity-attribute-value form. Such data are segregated according to data type to allow indexing by value when possible, and binary large object data are managed in the same way as other data. ACT/DB lets an investigator design a study rapidly by defining the parameters (or attributes) that are to be gathered, as well as their logical grouping for purposes of display and data entry. ACT/DB generates customizable data entry. The data can be viewed through several standard reports as well as exported as text to external analysis programs. ACT/DB is designed to encourage reuse of parameters across multiple studies and has facilities for dictionary search and maintenance. It uses a Microsoft Access client running on Windows 95 machines, which communicates with an Oracle server running on a UNIX platform. ACT/DB is being used to manage the data for seven studies in its initial deployment.

MOTIVATION: The increasing availability of biological databases on the World-Wide Web and hypertext links between them has made a wealth of information easily accessible to biologists. Additional retrieval capabilities can be achieved by storing explicitly specified biological relationships between different entities as discrete database entries. RESULTS: We have built CySPID, a prototype database about the cytoskeleton that explores the approach of explicitly representing biological relationships. The stored relationships are displayed along with other retrieved information, can be used to make hyperlinks to related entities, and can be used to search for entities with specified properties. CySPID is extensible in that new types of relationships may be created without altering the database schema. AVAILABILITY: CySPID is available for public use (http://ycmi.med.yale.edu/cyspid/). The CGI scripts used by CySPID are available upon request.

OBJECTIVE: To evaluate use of information resources during the first year of IAIMS implementation at the Yale-New Haven Medical Center. The evaluation asked: (1) Which information resources are being used? (2) Who uses information resources? (3) Where are information resources used? (4) Are multiple sources of information being integrated? DESIGN: Measures included monthly usage data for resources delivered network-wide, in the Medical Library, and in the Hospital; online surveys of library workstation users; an annual survey of a random, stratified sample of Medical Center faculty, postdoctoral trainees, students, nurses, residents, and managerial and professional staff; and user comments. RESULTS: Eighty-three percent of the Medical Center community use networked information resources, and use of resources is increasing. Both status (faculty, student, nurse, etc.) and mission (teaching, research, patient care) affect use of individual resources. Eighty-eight percent of people use computers in more than one location, and increases in usage of traditional library resources such as MEDLINE are due to increased access from outside the Library. Both survey and usage data suggest that people are using multiple resources during the same information seeking session. CONCLUSIONS: Almost all of the Medical Center community is using networked information resources in more settings. It is necessary to support increased demand for information access from remote locations and to specific populations, such as nurses. People are integrating information from multiple sources, but true integration within information systems is just beginning. Other institutions are advised to incorporate pragmatic evaluation into their IAIMS activities and to share evaluation results with decision-makers.

OBJECTIVE: To examine the issues involved in mapping an existing structured controlled vocabulary, the Medical Entities Dictionary (MED) developed at Columbia University, to an institutional vocabulary, the laboratory and pharmacy vocabularies of the Yale New Haven Medical Center. DESIGN: 200 Yale pharmacy terms and 200 Yale laboratory terms were randomly selected from database files containing all of the Yale laboratory and pharmacy terms. These 400 terms were then mapped to the MED in three phases: mapping terms, mapping relationships between terms, and mapping attributes that modify terms. RESULTS: 73% of the Yale pharmacy terms mapped to MED terms. 49% of the Yale laboratory terms mapped to MED terms. After certain obsolete and otherwise inappropriate laboratory terms were eliminated, the latter rate improved to 59%. 23% of the unmatched Yale laboratory terms failed to match because of differences in granularity with MED terms. The Yale and MED pharmacy terms share 12 of 30 distinct attributes. The Yale and MED laboratory terms share 14 of 23 distinct attributes. CONCLUSION: The mapping of an institutional vocabulary to a structured controlled vocabulary requires that the mapping be performed at the level of terms, relationships, and attributes. The mapping process revealed the importance of standardization of local vocabulary subsets, standardization of attribute representation, and term granularity.

Research groups within the Human Brain Project are developing technologies to help organize and make accessible the vast quantities of information being accumulated in the neurosciences. The goal of this work is to provide systems that enable this complex information from many diverse sources to be synthesized into a coherent theory of nervous system function. Our initial approach to this problem has been to create several small databases. While addressing the issues of each individual database, we are also considering how each might be incorporated into an integrated cluster of databases. In this paper, we describe a pilot project in which we construct a database of computational models of neuronal function. This database allows models to be created and run and their results reviewed through a World Wide Web interface. Because models encapsulate knowledge in a formal manner about how neuronal systems function, we also discuss how this database forms a natural center for our initial attempts at creating a cluster of related databases. General issues of database development in the context of the Web are also discussed.

This case study explored 1) how much online clinical data is required to obtain patient-specific recommendations from a computer-based clinical practice guideline, 2) whether the availability of increasing amounts of online clinical data might allow a higher specificity of those recommendations, and 3) whether that increased specificity is necessarily desirable. The "quick reference guide" version of the guideline for acute postoperative pain management in adults, developed by the Agency for Health Care Policy and Research, was analyzed. Patient-specific data items that might be used to tailor the computer's output for a particular case were grouped into rough categories depending on how likely they were to be available online and how readily they might be determined from online clinical data. The patient-specific recommendations were analyzed to determine to what degree the amount of text produced depended on the online availability of different categories of data. An examination of example recommendations, however, illustrated that high specificity may not always be desirable. The study provides a concrete illustration of how the richness of online clinical data can affect patient-specific recommendations, and describes a number of related design trade-offs in converting a clinical guideline into an interactive, computer-based form.

OBJECTIVE: To explore the software design issues involved in implementing an operational information sources map (ISM) knowledge base (KB) and system of navigational tools that can help medical users access network-based information sources relevant to a biomedical question. DESIGN: A pilot biomedical ISM KB and associated client-server software (ISM/Explorer) have been developed to help students, clinicians, researchers, and staff access network-based information sources, as part of the National Library of Medicine's (NLM) multi-institutional Unified Medical Language System (UMLS) project. The system allows the user to specify and constrain a search for a biomedical question of interest. The system then returns a list of sources matching the search. At this point the user may request 1) further information about a source, 2) that the list of sources be regrouped by different criteria to allow the user to get a better overall appreciation of the set of retrieved sources as a whole, or 3) automatic connection to a source. RESULTS: The pilot system operates in client-server mode and currently contains coded information for 121 sources. It is in routine use from approximately 40 workstations at the Yale School of Medicine. The lessons that have been learned are that: 1) it is important to make access to different versions of a source as seamless as possible, 2) achieving seamless, cross-platform access to heterogeneous sources is difficult, 3) significant differences exist between coding the subject content of an electronic information resource versus that of an article or a book, 4) customizing the ISM to multiple institutions entails significant complexities, and 5) there are many design trade-offs between specifying searches and viewing sets of retrieved sources that must be taken into consideration. CONCLUSION: An ISM KB and navigational tools have been constructed. In the process, much has been learned about the complexities of development and evaluation in this new environment, which are different from those for Gopher, wide area information servers (WAIS), World-Wide-Web (WWW), and MOSAIC resources.

This paper describes an approach that provides Internet-based support for a genome center to map human chromosome 12, as a collaboration between laboratories at the Albert Einstein College of Medicine in Bronx, New York, and the Yale University School of Medicine in New Haven, Connecticut. Informatics is well established as an important enabling technology within the genome mapping community. The goal of this paper is to use the chromosome 12 project as a case study to introduce a medical informatics audience to certain issues involved in genome informatics and in the Internet-based support of collaborative bioscience research. Central to the approach described is a shared database (DB/12) with Macintosh clients in the participating laboratories running the 4th Dimension database program as a user-friendly front end, and a Sun SPARCstation-2 server running Sybase. The central component of the database stores information about yeast artificial chromosomes (YACs), each containing a segment of human DNA from chromosome 12 to which genome markers have been mapped, such that an overlapping set of YACs (called a "contig") can be identified, along with an ordering of the markers. The approach also includes 1) a map assembly tool developed to help biologists interpret their data, proposing a ranked set of candidate maps, 2) the integration of DB/12 with external databases and tools, and 3) the dissemination of the results. This paper discusses several of the lessons learned that apply to many other areas of bioscience, and the potential role for the field of medical informatics in helping to provide such support.

Building a map of restriction sites from double-digest gel data can be a complex and frustrating task, especially when many DNA fragments are detected or when the gel results are ambiguous. 'Double Digester' is an interactive, graphical computer program which helps researchers understand and resolve such data. It explicitly represents the experimental data, the associated uncertainties, the researcher's hypotheses and possible map interpretations. Alternative solutions are frequently possible, and the differences between them may help determine which additional experiments might resolve ambiguities. Initial use has confirmed the benefits of this approach, and has suggested ways in which it can be refined and extended. Double Digester meets the need for a practical tool to help build restriction maps, and also illustrates how a computer-based tool can confront experimental uncertainty in an integrated fashion.

It is widely accepted that parallel computers, which have the ability to execute different parts of a program simultaneously, will offer dramatic speed-up for many time-consuming biological computations. The paper describes how the use of the machine-independent parallel programming language, Linda, allows parallel programs to run on an institution's network of workstations. In this way, an institution can harness existing hardware, which is often either idle or vastly underutilized, as a powerful 'parallel machine' with supercomputing capability. The paper illustrates this very general paradigm by describing the use of Linda to parallelize three widely used programs for genetic linkage analysis, a mathematical technique used in gene mapping. The paper then discusses a number of technical, administrative and social issues that arise when creating such a computational resource.

A platform program that performs biological sequence comparison provides a case study to compare the relative advantages of a machine-independent approach to parallel computation versus a machine-specific approach. The program consists of two routines: (i) PSCANLIB, which compares a single biological sequence against a database of sequences, and (ii) PCOMPLIB, which compares a database of sequences against another database of sequences, or against itself. The program was first parallelized to run on the Intel Hypercube parallel computer using native Hypercube commands to coordinate the parallel computation. The parallelization logic of the program was then translated into a machine-independent parallel programming language, Linda. These two approaches to parallelization are contrasted in terms of: (i) the expressive power of the logic that coordinates the parallel computation, (ii) the portability of the machine-independent version to other parallel machines and (iii) the relative efficiency of the two versions of the program. In the benchmark tests reported, the benefits of the machine-independent approach were achieved with only a modest sacrifice in efficiency.

The SENEX project is exploring knowledge representation in the neurobiology of ageing through object-oriented programming. SENEX is built from a classification structure of biologic entities and significant relationships among them. For example, an enzyme is an entity and an enzymatic reaction is a relationship among enzyme, cofactor(s), substrate(s) and product(s). There are currently 2600 classes of entities and 50 classes of relationships in SENEX. The class structure serves several functions. One function is to interrelate general and specific categories of molecular and morphologic entities. For example, tyrosine kinase and serine/threonine kinase are specific types of the more general class of protein kinase enzymes. Another function of the class structure is to serve as a network through which inheritance of attributes may occur. For example, the attribute 'subunits' is inherited by all subclasses of the general class multisubunit protein. Information may be accessed through links established in the class structure and through links relating one object as part of another. Relationships form the basis of separate modules within SENEX. This paper describes the types of relationships currently used and planned in the representation of age-related changes in cellular signal transduction processes of mammalian central nervous systems. We also describe tools for specific retrieval of relationships and for tracing links in complex reaction cascades. Application of these tools to identifying possible signal transduction pathways to guide further exploration through experimentation is discussed.

We have parallelized the FASTA algorithm for biological sequence comparison using Linda, a machine-independent parallel programming language. The resulting parallel program runs on a variety of different parallel machines. A straight-forward parallelization strategy works well if the amount of computation to be done is relatively large. When the amount of computation is reduced, however, disk I/O becomes a bottleneck which may prevent additional speed-up as the number of processors is increased. The paper describes the parallelization of FASTA, and uses FASTA to illustrate the I/O bottleneck problem that may arise when performing parallel database search with a fast sequence comparison algorithm. The paper also describes several program design strategies that can help with this problem. The paper discusses how this bottleneck is an example of a general problem that may occur when parallelizing, or otherwise speeding up, a time-consuming computation.

The ATTENDING system is designed to critique a physician's preoperative plan for anesthetic management. In undertaking to critique a physician's plan, ATTENDING differs from other medical decision making systems, which in effect attempt to tell a physician what to do. ATTENDING's approach may prove more acceptable clinically, and may avoid certain social, medical, and medicolegal drawbacks. To cri-tique a physician's plan, ATTENDING must confront three basic problems. 1) It must be able to explore flexibly all possible approaches for a patient's management. The formalism of an ``augmented decision network'' allows this. 2) It must be able to assess the relative risks and benefits of alternative approaches intelligently. A heuristic approach to risk analysis is outlined, based on three basic principles which are de-scribed in detail. 3) It must produce a potentially complex analysis which critiques the plan in focused, readable prose. This is facilitated by PROSENET, an approach which allows clean separation between the organization of the content of an analysis and its expression in English prose.