Guest post by Dr. Valerie Florance, Director of the NLM Division of Extramural Programs.
How can you discover the cool stuff happening in translational bioinformatics?
Check out Russ Altman’s “Year in Review.”
Each year since 2007, Altman, a professor of bioengineering, genetics, medicine, and biomedical data science at Stanford, has hosted a popular plenary session at the annual joint summit on Clinical Research Informatics and Translational Bioinformatics sponsored by AMIA, the American Medical Informatics Association. Entitled “Translational Bioinformatics: The Year in Review,” this lively talk provides an overview of scientific trends and publications, celebrating progress and highlighting opportunities in research focused on informatics and data science methods that link biological entities to clinical entities.
It’s not exactly the Emmys, but Altman’s talk does bestow minor celebrity status on those acknowledged—and more importantly, it draws attention to new data collections, new software tools, and new directions for a field that increasingly impacts real problems in biology and medicine.
To build the list of candidate papers, Altman solicits recommendations from scientific colleagues. He then enlists volunteers from AMIA’s student working group to review and score the articles based on three basic criteria: informatics novelty, application importance, and presentability.
This year the student team scored over 285 articles, out of which Altman chose 28 to present at the joint summit and another 32 to receive a shout-out. This and past years’ honorees are enshrined on Altman’s blog.
After each meeting, NLM’s grant program staff comb through Altman’s presentation to identify work that builds on or highlights NLM-funded research. The process is not exact, relying on the articles’ acknowledgements of support to identify funding sources, but, like Altman’s talk itself, it’s something to go on, a back-of-the-envelope way of seeing where NLM dollars are having an impact and driving the science forward.
Of course, these papers commonly have multiple authors and multiple sources of funding. Furthermore, for the data scientists and informaticians supported by NLM, their methodological work may not be the focus of the article, but that work nevertheless contributes fundamentally to the reported results.
Of the 28 articles Altman presented this year, five acknowledged NLM grant support, while five additional NLM grantees secured one of Altman’s 32 shout-outs [total: 10 of 60 (16.7%)].
The following three examples from Altman’s list give a sense of the kind of work—and impact—NLM grants and grantees are having.
- Ioannidis, Nilah M., et al. “REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.” American Journal of Human Genetics4 (2016): 877–885.
This research was funded by NIH grants from four different NIH Institutes, and by the Intramural Research Program of the NIH National Human Genome Research Institute.
Although Sean Mooney is not listed as a co-author, the article acknowledges his NLM-funded grant, “Informatic Profiling of Clinically Relevant Mutation” (1R01LM009722). Initially issued in 2007, Dr. Mooney’s award is still active and has produced 53 publications with more than 780 citations (Thomson Reuters).
The funded research develops novel methods to identify patients at risk for complex trait disorders, with a long-term goal of creating a whole genome interpretation engine based on data in public resources. Dr. Mooney’s academic background is computational modeling and biochemistry.
- Kim, Dokyoon, et al. “Using Knowledge-driven Genomic Interactions for Multi-omics Data Analysis: Metadimensional Models for Predicting Clinical Outcomes in Ovarian Carcinoma.” Journal of the American Medical Informatics Association3 (2017): 577-587.
This paper acknowledges support from three different NIH Institutes and the National Science Foundation.
Co-author Marylyn Ritchie received NLM funding from 2009-2013 for the grant “Analysis Tool for Heritable and Environmental Network Associations” (1R01LM010040). Her work resulted in 33 publications with more than 151 citations (Thomson Reuters).
The project helped develop the ATHENA framework, which uses machine learning to incorporate biological information from databases with diverse data types to detect disease susceptibility driven by gene-gene and gene-environment interactions. Dr. Ritchie’s academic background is applied statistics and statistical genetics.
- Bagley, Steven C., et al. “Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants.” Ed. Maricel Kann. PLoS Computational Biology4 (2016): e1004885.
This work was funded by five different NIH Institutes and Pfizer.
The paper acknowledges Russ Altman’s NLM grant currently focused on “Text Mining for High-Fidelity Curation and Discovery of Gene-Drug-Phenotype Relationships” (5R01LM005652). There are 98 publications attributed to this grant, with 620 citations (Thompson Reuters).
Altman, an MD, PhD with academic training in molecular biology and medical information sciences, uses computational natural language processing to extract semantically precise knowledge about drugs, genes, and phenotypes.
In 1997, Altman’s research earned him the Presidential Early Career Award for Scientists and Engineers (PECASE), the first NLM grantee to earn that distinction.
Now, twenty years later, Dr. Altman is encouraging other scientists by featuring some of the innovative work happening in translational bioinformatics.
Whether funded by NLM or not, we expect this research to move the science forward, and who knows? Maybe something more than an Altman lies in the future for these researchers. Stay tuned.