Disease is an ongoing process, so Michael Liebman says biomedical informatics tools are required
Michael Liebman directs computational biology at the Abramson Family Cancer Research Institute and is Abramson Investigator at the University of Pennsylvania School of Medicine. He is also an adjunct professor of biomedical engineering at Drexel University and is CSO for ProSanos. He welcomes comments at [email protected]
This industry’s (too?) many meetings are evidence of the significant effort to define the Post-Genome period, functional genomics, proteomics, and so forth. It’s time to anticipate the punctuated evolution from a focus on bioinformatics to one on biomedical informatics.
I currently define bioinformatics (a dangerous undertaking) as the catalyst for converting molecular/genomic information into biological process/ pathway knowledge. This differs from how I used it in 1987 when I was examining the sequence-structure-function relationship of proteins at the amino acid level, but more accurately reflects its current stage of evolution.
To define biomedical informatics we might have to refine our view of disease. The evolution from risk (genetics/genotype) to disease (expressed phenotype) should be viewed as a continuum, not as distinctly separable states, and disease itself should be viewed as an ongoing process, not a state fixed in time. This is an important distinction from the current application of diagnostics and therapeutic intervention and will impact the drug development process. Biomedical informatics requires access to longitudinal patient medical histories, not simply clinical trial data.
If we add clinical data to current bioinformatic practices, we establish the following relationships:
• Clinical Observations + Molecular/Genetic Information = Clinical Correlations
• Clinical Observations + Biological Process/Pathway Knowledge = Clinical Mechanism
Clinical correlation points us in the right direction, but clinical mechanism directs us to the best target for diagnostic or therapeutic development. Biomedical informatics is the catalyst for the conversion from correlation to mechanism. While bioinformatics provides the fundamental knowledge about general biological processes, it is biomedical informatics, with the inclusion of clinical observations, which enables this knowledge to be brought to bear on drug and diagnostic development and ultimately, clinical practice. Its value cannot be underestimated.
Some of the challenges facing biomedical informatics include: 1) patient records are not universally available in electronic form; 2) soft data: clinical observations may be qualitative in nature; 3) quantitative results may require significant detail about the underlying test and reagents used; 4) medical terminology may be ambiguous across different specialties; 5) patient confidentiality must be maintained; 6) patient consent must be obtained for data use in a particular study; 7) diseases as we know them today are typically composites of multiple subtypes that reflect an individual’s genetic makeup and response; 8) diseases are frequently observed well beyond their initiation, which results in co-morbidities and lessened ability to treat effectively; 9) disease etiologies require synchronization of patient records, which is not currently available for most diseases; and 10) methodologies evolve as do standards of care, practice guidelines, diagnostic procedures, etc. Many of these have analogies in the bioinformatics domain.
Biomedical informatics is in its early stages of evolution — about equivalent to bioinformatics in 1987. A survey of the literature shows that by mid-year 2001, there were a total 1,974 papers published using bioinformatics (928 in 2000 alone), and 74 papers using biomedical informatics (nine in 2000 alone). It would have been hard to predict in 1987, when 12 papers had been published, several on NMR, where we would be today in bioinformatics, let alone biomedical informatics in 2015.
Biomedical informatics will and should evolve to address the needs of the community and the advances in technology and science that will occur simultaneously. Is that bad? No, that’s just good science.
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