A Search Method to Support Temporal Transcriptome Analysis.

Recently, there has been an increasing interest in mining time series databases with a focus on data representation. We propose a hybrid statistical and temporal logic model to yield search pattern that can be used for pattern matching and database search to identify novel and common patterns in temporal expression experiments. The method accounts for various challenges that can be found in publicly available gene expression databases.

AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master's degree level.

This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master's level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends.

Finding temporal gene expression patterns for translational research.

Translational research of time-series of gene-expression microarray datasets makes use on gene expression profiles that have been obtained at different points in time. Our web-based multi-user program helps a researcher find temporal patterns like peaks in large pre-selected microarray data sets that include data from different but related studies in publicly available databases. If all studies use the same platform, data can be combined for a meta-analysis type approach. For combination of data from different platforms we allow only Affymetrix GeneChips, for which a method for pooling of information exists. To search for time patterns, the data are transformed into an abstract layer that is independent from the particular selection of time point in the individual studies.

Translational meta-analysis tool for temporal gene expression profiles.

Widespread use of microarray technology that led to highly complex datasets often is addressing similar or related biological questions. In translational medicine research is often based on measurements that have been obtained at different points in time. However, the researcher looks at them as a progression over time. If a biological stimulus shows an effect on a particular gene that is reversed over time, this would show, for instance, as a peak in the gene's temporal expression profile. Our program SPOT helps researchers find these patterns in large sets of microarray data. We created the software tool using open-source platforms and the Semantic Web tool Protégé-OWL.

SPOT--towards temporal data mining in medicine and bioinformatics.

Mining large clinical and bioinformatics databases often includes exploration of temporal data. E.g., in liver transplantation, researchers might look for patients with an unusual time pattern of potential complications of the liver. In Knowledge-based Temporal Abstraction time-stamped data points are transformed into an interval-based representation. We extended this framework by creating an open-source platform, SPOT. It supports the R statistical package and knowledge representation standards (OWL, SWRL) using the open source Semantic Web tool Protégé-OWL.

A method to use microarray and clinical data in sequential classification.

We investigate the problem of constructing a sequential classification procedure allowing for a classification into two (risk) groups at an average minimum cost or at the earliest possible time. The quality of the procedure is maintained by specified upper bounds for the conditional errors of the entire procedure.