Biomedical subjects

Prakash Nadkarni

Publications and source records attributed to Prakash Nadkarni.

7 recordsLinked to original sources

PubMed2006

Pivoting approaches for bulk extraction of Entity-Attribute-Value data.

Entity-Attribute-Value (EAV) data, as present in repositories of clinical patient data, must be transformed (pivoted) into one-column-per-parameter format before it can be used by a variety of analytical programs. Pivoting approaches have not been described in depth in the literature, and existing descriptions are dated. We describe and benchmark three alternative algorithms to perform pivoting of clinical data in the context of a clinical study data management system. We conclude that when the number of attributes to be returned is not too large, it is feasible to use static SQL as the basis for views on the data. An alternative but more complex approach that utilizes hash tables and the presence of abundant random-access-memory can achieve improved performance by reducing the load on the database server.

Algorithms↗

PubMed2004

VitaPad: visualization tools for the analysis of pathway data.

MOTIVATION: Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data. RESULTS: VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance. AVAILABILITY: http://bioinformatics.med.yale.edu CONTACTS: matthew.holford@yale.edu; hongyu.zhao@yale.edu.

Algorithms↗

PubMed2004

QIS: A framework for biomedical database federation.

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.

Computer Communication Networks↗

PubMed2003

The Cancer Genetics Network: recruitment results and pilot studies.

OBJECTIVE: The National Cancer Institute established the Cancer Genetics Network (CGN) to support collaborative investigations into the genetic basis of cancer susceptibility, explore mechanisms to integrate this new knowledge into medical practice, and identify ways of addressing the associated psychosocial, ethical, legal, and public health issues. SUBJECTS AND METHODS: The CGN has developed the complex infrastructure required to support the projects, including the establishment of guidelines and policies, uniform methods, standard questionnaires to be used by all of the centers, and a standard format for submission of data to the Informatics Center. Cancer patients and their family members have been invited to enroll and be included in a pool of potential study participants. The Information Technology Group is responsible for support of the design, implementation, and maintenance of the multicenter Network-wide research protocols. RESULTS: As of January 2004, the CGN contained data on 23,995 probands (participants) and 425,798 family members. As a resource for cancer genetic studies, the CGN has a large number of probands and first-degree relatives with and without cancer and with multiple ethnicities. Different study designs can be used including case-control, case-case, and family studies. CONCLUSIONS: The unique resources of the CGN are available for studies on cancer genetic susceptibility, translational research, and behavioral research. The CGN is now at a point where approved collaborators may have access to enrolled patients and their families for special studies, as well as to the clinical, environmental and family cancer history data banked in the Informatics Center.

Female↗

PubMed2003

Creating knowledgebases to text-mine PUBMED articles using clustering techniques.

Knowledgebase-mediated text-mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing

Algorithms↗

PubMed2002

Designing and implementing special-purpose databases: lessons from the pharmacogenetic network.

The Pharmacogenetics Research Network, which has the long-term goal of genotype-phenotype correlation related to pharmacotherapy, mandates timely electronic publication of results by participating research groups through submission to PharmGKB, the consortium's repository database. Because informatics expertise across groups varies, many groups need help in managing their own data and in generating electronic submissions. To assist these operations, we perform a needs assessment to determine an optimum database implementation strategy, which varies from standalone microcomputer database application to Web-based solutions, depending on the group and problem scope. Solution implementation is coupled with transfer of expertise through hands-on training, so as to reduce the groups' long-term dependence on us. Where multiple groups face common problems, such as managing genotyping data or clinical study support, we have devised generic software that can be reused in its entirety by individual groups, or customized with modest effort.

Computer Communication Networks↗

PubMedDate not supplied

Integrating query of relational and textual data in clinical databases: a case study.

OBJECTIVES: The authors designed and implemented a clinical data mart composed of an integrated information retrieval (IR) and relational database management system (RDBMS). DESIGN: Using commodity software, which supports interactive, attribute-centric text and relational searches, the mart houses 2.8 million documents that span a five-year period and supports basic IR features such as Boolean searches, stemming, and proximity and fuzzy searching. MEASUREMENTS: Results are relevance-ranked using either "total documents per patient" or "report type weighting." RESULTS: Non-curated medical text has a significant degree of malformation with respect to spelling and punctuation, which creates difficulties for text indexing and searching. Presently, the IR facilities of RDBMS packages lack the features necessary to handle such malformed text adequately. CONCLUSION: A robust IR+RDBMS system can be developed, but it requires integrating RDBMSs with third-party IR software. RDBMS vendors need to make their IR offerings more accessible to non-programmers.

Abstracting and Indexing↗