Abstract: The evolution of the modern electronic health record (EHR) has created an environment in which large amounts of medical data are collected reflecting the character of disease processes and the response of caregivers to these processes. This data has historically had an episodic character but now is rapidly becoming longitudinal in nature, allowing it to reflect the course of health over time. The presence of this data facilitates both the study of disease processes over time and the development of novel, experience-based approaches to support the delivery of care.

The data available is captured both as structured, readily-computable information and as textual reports, which require approaches base on natural language processing (NLP) paradigms for effective, computer use. In this presentation we will discuss our ability to extract this data and to use it to develop computable models of disease capable of informing care over time. The conjunction of data mining and artificial intelligence techniques holds promise to change the way we deliver, document, and evaluate the clinical care process.

Title: Metabolomics: The Next Frontier for Bioinformatics?

Abstract: Metabolomics is a newly emerging field of “omics” research concerned with the high-throughput identification and quantification of the small molecule metabolites in the metabolome. Metabolomics is drawing considerable attention these days because it has the potential to substantially improve the speed and accuracy of many clinical tests and diagnoses. Metabolomics shares many of the same computational needs with other, much better established “omics” fields such as genomics, proteomics and transcriptomics. All four “omics” techniques require electronically accessible and searchable databases, all of them require software to handle or process data from their own high-throughput instruments, all of them require laboratory information management systems (LIMS) to manage their data, and all require software tools to predict properties, pathways, relationships or functions. Unfortunately, for metabolomics specialists, relatively few of these essential tools or resources exist. Fortunately, for bioinformaticians, this represents a wonderful opportunity to apply what they have learned from other “omics” endeavors to this newly emerging field. In this presentation I will highlight some of the efforts we, and others are making to bring modern bioinformatics practices to metabolomics – including the development of public metabolomic databases, the development of LIMS and the creation of software to interpret metabolomic data.