Scientific Goal:
Carry out research in
knowledge sharing, reuse, and reproducibility
that improves applications in biomedicine & health
Why does it matter?
Scientists in both health care and biology often encounter challenges in sharing and re-using data and knowledge. If a scientific result is not shareable and reproducible, its value is lower.
For example, without good health record standards that cross medical institutions, research on rare diseases would be impossible (because there are not enough patients with a rare disease at a single institution.)
Similarly, if a new system for data analysis is created that only works with one data set in one environment, researchers are forced to "re-invent the wheel" to apply the new system to a new place or dataset.
My research aims to avoid these problems, and thus increase the speed and efficiency of researchers who might be (for example) working to improve cancer diagnosis and care.
And just what the heck is Biomedical & Health Informatics??
Biomedical Informatics is the science underlying the acquisition, maintenance, retrieval, and application of biomedical knowledge and information to improve patient care, medical education, and health science research
Since it is a science, it should not be confused simply with applying information technology to the medical domain.
(credit to Charles Friedman for this idea)
I would also add that a primary feature of Biomedical and Health Informatics is its interdisciplinary nature: It connects computer science, medicine, biology and health care, and provides a synergy that goes beyond anything that researchers in any single domain can provide.
I am interested in the development and use of quality ontologies for biomedical & health informatics.
Hmmm. And what is an ontology anyways?
I prefer a broad and operational definition of this term: any schema that helps a group of people organize and understand the terms and relationships in a particular domain of interest. Ontologies are particularly important for Knowledge Sharing and Knowledge Reuse.
I also love that any definition of ontology quickly veers into philosophy: An ontology is a systematic account of existence, a theory of "what exists". It is "an explicit specification of a conceptualization" (Credit to Tom Gruber, 1993).
See also my Teaching page for some information about my Knowledge Representation course, which includes this topic.
A challenge for knowledge representation are processes. Biomedical data is usually captured at a single point in time. However, often we are interested in biological (or pathological) trajectories over time. What is the mechanism of disease? How does biology (and disease progression) change over time? How should we treat chronic diseases over time?
My research directions (past and current) often relate to these sort of questions.
Subcellular pathways (metabolic and signaling processes, for example) How do we store, analyze, and share our knowledge about these subcellular processes? Although there is some research to build from in systems biology, one challenge is how to unify pathway knowledge across many different resources and libraries of pathways.
Biosimulation models There is a community of researchers who develop mathematical models that can be used to simulate biological (and pathological) processes. Hopefully, the impact of these models is to understand the mechanisms of disease, so that they can be better understood and treated.
Often, these models focus on ordinary differential equation (ODE) models of subcellular processes, but the broad community includes modelers interested in multi-cell models, electro-cardio physiology, neuronal models, etc. I participate in the "COmputational Modeling in BIology NEtwork” (COMBINE) community (which has annual meetings, etc), to help standardize how we describe these models, so that these models are reproducible, and so that we can better share and reuse these models.
Health care guidelines / protocols How can we capture standards for the process of health care? (And should we even try to standardize?). Note that guidelines aim to capture, at a high level, best practices for care of particular diseases, whereas clinical trial protocols describe the precise clinical processes that must be followed for a scientific study. In both cases, clinical process representation is interesting and challenging.
An important use of standardization (and ontologies) is to assist in the reproducibility of systems and scientific work. Reproducibility is a cornerstone of science: If published results are not reproducible, one cannot build from the work of others. Ontologies help by making sure researchers use consistent terminology to describe their data and results, which increases understandability. (And one must understand a result before one can reproduce that result!)
I am an investigator in the NIH-funded Center for Reproducible Biomedical Modeling, which aims to help researchers build understandable, reproducible, reusable, and composable models (mostly in the realm of systems biology). The long term goal is to build predictive models that can guide precision medicine and synthetic biology.
The Center promotes scientific work that is "FAIR": Findable, Accessible, Interoperable and Reusable. This is currently a "hot slogan" in science, e.g., see the FAIRdom web pages.