COVID-19 Research by BMIR
The Stanford Center for Biomedical Informatics Research (BMIR) is leading data science efforts to address the COVID-19 pandemic—to enhance patient care, to measure the scale of the infection, and to model the demand on healthcare resources.
Many scientists within BMIR are involved, including Mark Musen, Manisha Desai, Tina Hernandez-Boussard, and Nigam Shah. Shah is shown here in a recent video from the Department of Medicine Grand Rounds, where he discusses modeling for COVID-19 planning and response: https://www.youtube.com/watch?v=GgaY099XqlI
Software Developed at BMIR Contributes to New Cancer-Drug Target
Dr. Olivier Gevaert and his team at the Stanford Center for Biomedical Informatics Research (BMIR) are part of the team who developed a framework for software tools for network biology and medicine that has been successfully applied to discover novel drug targets for two aggressive diseases: glioblastoma and hepatocellular carcinoma. AMARETTO is a data-driven platform for diagnostic, prognostic, and therapeutic decision-making in cancer.
The Gevaert Lab at BMIR developed the initial AMARETTO algorithm, which has been expanded in collaboration with the Pochet Lab at Harvard and the GenePattern team led by Dr. Jill Mesirov at UCSD. AMARETTO is now available as both an R package, a GenePattern module and a Jupyter notebook, thus reaching a wider audience. Moreover, extensive functionally has been added to interpret the output of AMARETTO by providing links to molecular pathways, clinical and imaging phenotypes and validation of the predictions using perturbation experiments from the LINCS project.
Taken together, AMARETTO has now been enabled for use by a wide range of researchers—including basic and clinical scientists—at Stanford, Harvard, and UCSF, and is used to discover and validate novel drug targets in a wide range of human cancers.
The cancer research was the subject of a blog post on the National Cancer Institute’s website entitled “Informatics Technology for Cancer Research Program Drives and Fosters Community of Cancer Informatics Researchers: An AMARETTO Tool Success Story.” The article was written by Nathalie Pochet, PhD, Assistant Professor in the Department of Neurology, Harvard Medical School.
Is repeat blood-testing necessary? AI could help decide
You want and expect your doctor to be thorough, but over-testing can raise concerns as well, can you be too thorough? Jonathan Chen, MD, Ph. D., Assistant Professor of Medicine, believes that the answer is yes, especially when looking at blood diagnostic testing. Blood tests are an important diagnostic tool, but often repeated tests yield diminished results. Repeated tests not only often show no change, but they are uncomfortable for the patient. Couple this with concerns about rising healthcare expenses and it is clear that a solution needs to be found.
Machine learning algorithms can synthesize volumes of electronic medical record data to systematically identify low yield tests, quantifying the predictability of results to encourage high-value care. Chen and a team of researchers and physicians have taken steps to try and address the problem and have created an algorithm that can predict whether a given blood test will come back "normal." Their work was recently published in the JAMA Network Open. This algorithm can tell doctors if a repeat test will be likely to produce a result that is different from the original test. These algorithms can often predict >95% chance that a test will yield normal results, but it is a separate issue for laboratorians, clinicians, and patients to decide in individual cases whether it is *worth* checking for something when you were already 95% sure of the answer.
Dr. Zihuai He has received an NIH RO1 grant to develop methods to analyze Alzheimer's disease genetics and contribute to the development of new treatment opportunities.
The NIH research grant will support the research group led by Dr. Zihuai He to develop innovative methodologies for the analysis of non-coding variants, combining whole-genome sequencing, epigenetic technologies and multi-layered phenotypic data such as imaging and biomarkers. The proposed methods will be applied to a total of roughly 20,000 whole genomes unifying:
• The Alzheimer's Disease Neuroimaging Initiative (ADNI)
• The Alzheimer's Disease Sequencing Project (ADSP)
• The Religious Orders Study and Memory and Aging Project (ROSMAP),
• A newly established cohort, the Stanford Extreme Phenotypes in Alzheimer's Disease (StEP AD)
Dr. Olivier Gevaert to Chair AMIA Biomedical Imaging Working Group
Dr. Olivier Gevaert has been named Chair-Elect of the Biomedical Imaging Working Group of the American Medical Informatics Association (AMIA). The Group focuses on all forms of biomedical data—radiological studies, pathology, photos and more. The working group will showcase its work at AMIA conferences including the Clinical Informatics Summit in March 2020 in Houston and the annual symposium in Chicago in November.
The Biomedical Imaging Informatics Working Group has a four-fold mission:
• to catalyze the development of computational methods for improving the quantification, representation, and clinical translation of images and imaging-derived information,
• to advocate the evolution and adoption of multimedia electronic health through the seamless integration of imaging and other clinical data,
• to identify opportunities for leveraging imaging through the development and validation of new quantitative biomarkers for personalizing care, and.
• to act as a resource for members wishing to receive training or education in biomedical imaging informatics.
The CEDAR project has been chosen as a key participant in the GO FAIR Metadata for Machines (M4M) workshops and the FAIR Funder pilot program. The GO FAIR initiative, an international program to advance FAIR (findable, accessible, interoperable, reusable) data and services, launched the M4M workshop series to stimulate the creation and re-use of FAIR metadata standards and machine-ready metadata templates. The M4M workshops have adopted CEDAR to provide the metadata capabilities for the initial workshops.
The M4M workshops are agile events that bring together domain experts, metadata specialists, and technical developers to define metadata elements and standards, create machine-actionable templates for collecting metadata according to those standards, and register the templates for open access, discovery, and re-use. CEDAR has worked with the Leiden-based GO FAIR team for over a year to demonstrate and evaluate the CEDAR technologies for these tasks, and participated in two recent M4M workshops. At the second of these, two national science funders—the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW)—took the first steps toward a complete life cycle of FAIR metadata for their communities by creating CEDAR templates to capture metadata of interest to research funding agencies.
In coming months, CEDAR expects to participate in several more M4M workshops, and advance the FAIR Funder pilot program (depicted in the diagram) through many more of the 7 stages shown in the diagram. CEDAR initially provided services to define metadata elements (1), create machine-actionable templates (2), and register the templates in the CEDAR repository for later re-use (3), and CEDAR enhancements already under way will provide advanced search and open publication for the CEDAR metadatda resources that have been developed, and integrated metadata authoring in coordination with the DSL Data Wizard (4). Also important to GO FAIR: CEDAR already provides rigorous semantic capabilities that let CEDAR metadata be published as JSON-LD or simple RDF triples, two highly interoperable formats for sharing research metadata.
The CEDAR technologies adopted by GO FAIR's M4M workshops and the FAIR Funder pilot program are helping the GO FAIR community move metadata management from ad-hoc, individually developed solutions to a more rigorous, structured, and user-friendly approach to metadata.
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