Grants and Funding: Extramural Programs (EP)
NLM Grantee Spotlight PECASE Awards
AMIA New Investigator Award recognizes an individual’s early informatics contributions and significant scholarly contributions on the basis of scientific merit and research excellence. Dr. Shah received the award because of his significant scientific productivity in informatics prior to eligibility for fellowship in the College of Informatics, multiple significant scientific publications, and demonstrated commitment to AMIA. Dr. Shah is an Assistant Professor of Medicine at the Stanford School of Medicine. His research is focused on combining machine learning and text-mining with knowledge encoded in medical ontologies to learn practice-based evidence from unstructured data. Dr. Shah is actively engaged in teaching and mentoring students. He chaired the American Medical Informatics Association’s Summit on Translational Bioinformatics in 2012. Dr. Shah serves on the editorial board of the Journal of Biomedical Informatics, BMC Bioinformatics, Nature Scientific Data and serves as a guest editor for the Journal of the American Medical Informatics Association. Dr. Shah also serves as scientific advisor to companies applying semantic technologies in the health and life sciences. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
Nature news on Dr. Cardozo’s research http://www.nature.com/news/project-ranks-billions-of-drug-interactions-1.14245.
Dr. Cardozo is Associate Professor of Biochemistry and Molecular Pharmacology at NYU School of Medicine (NYUSOM). An active clinician, educator and computational structural biologist specializing in drug/vaccine design and protein engineering, Dr. Cardozo has been funded both by the Bill and Melinda Gates Foundation and the NIH. He has developed the first known inhibitors of several challenging drug targets. Dr. Cardozo was awarded a "Grand Opportunities" ARRA award to develop a novel chemical biology network that can match biomarkers of complex diseases to drugs. Because of his diverse background in medicine, biology, surgery, chemistry and computer science, Dr. Cardozo was recognized with a 2008 NIH Director's New Innovator Award and was recently awarded the NIDA Avant-Garde Award for HIV/AIDS Research. He serves on the National Library of Medicine Biomedical Library and Informatics Review Committee. At NYUSOM, he serves as Graduate Advisor for the Computational Biology Program. He also currently serves on the Young and Early Career Investigator Committee for the Global HIV Enterprise. Dr. Cardozo received his MD-PhD from NYU School of Medicine.
A Chemical Biological Network for Personalized Medicine
New York University School of Medicine
Dr. Mandl and his colleagues created a score to help patients decide when to visit a clinician for the evaluation of a sore throat. Dr. Mandl and his team analyzed 71,776 patients aged 15 years or older with pharyngitis who visited a clinic from September 2006 to December 2008. If a patient aged 15 years or older with a sore throat did not visit a clinician when the new score estimated the likelihood of group A streptococcal (GAS) pharyngitis to be less than 10%, 203,000 visits would be avoided in the United States each year. Similarly, using the score, 8,500 patients with GAS pharyngitis who would have received antibiotics would not be treated with them. The study is published in the November 5, 2013 issue of the Annals of Internal Medicine. The study is related to research funded through Dr. Mandl's 2004 Presidential Early Career Award for Scientist and Engineers (PECASE), supported by NLM. (http://www.nlm.nih.gov/ep/PECASE.html)
Andrew M. Fine, MD, MPH; Victor Nizet, MD; and Kenneth D. Mandl, MD, MPH. Participatory Medicine: A Home Score for Streptococcal Pharyngitis Enabled by Real-Time Biosurveillance: A Cohort Study Ann Intern Med.2013;159(9):577-583.doi:10.7326/0003-4819-159-9-201311050-00003 http://annals.org/article.aspx?articleid=1763228
Dr. Atul Butte
Dr. Butte was selected because of his tremendous work and leadership, and was honored at an event at the White House featuring the Open Science Champions of Change. The event highlighted outstanding individuals, organizations, or research projects promoting and using open scientific data and publications to accelerate progress and improve our world.
Atul Butte, MD, PhD is Chief of the Division of Systems Medicine and Associate Professor of Pediatrics, Medicine, and by courtesy, Computer Science, at Stanford University and Lucile Packard Children's Hospital. Dr. Butte trained in Computer Science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in Pediatrics and Pediatric Endocrinology at Children's Hospital Boston, and then received his PhD in Health Sciences and Technology from Harvard Medical School and MIT.
The Butte Laboratory at Stanford builds and applies tools that convert more than 300 billion points of molecular, clinical, and epidemiological data measured by researchers and clinicians over the past decade into diagnostics, therapeutics, and new insights into disease. The Butte Laboratory currently has been funded by HHMI and under sixteen NIH grants.
Dr. Butte has authored more than 100 publications and delivered more than 120 invited presentations in personalized and systems medicine, biomedical informatics, and molecular diabetes, including 20 at the National Institutes of Health or NIH-related meetings.
Dr. Butte is a former trainee at NLM's Biomedical Informatics Training Program at Harvard Medical School and MIT. He is currently the PI on NLM grant 5R01LM009719-04.
Dr. Butte presenting at TEDMED
Dr. Jason Moore
Dr. Jason Moore and his colleagues have helped to discover three unique genetic variations that influence body size and obesity in people of African ancestry. The study was published online in the April 2013 edition of Nature Genetics http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2608.htm. The large-scale genetic analysis demonstrated the utility of examining ancestrally diverse populations for clues as to why some groups seem more prone than others to physical problems such as obesity.
Despite their well-documented benefits, statins, drugs used to lower cholesterol, are commonly discontinued in routine care. Dr. Turchin and his team analyzed structured and free text clinical data from 107,835 patients between 2000-2008, who were prescribed a statin. They analyzed statin discontinuation and identified patients who had statin-related events (possible side effects to statins), whether people stopped taking their statins after these events, whether they later restarted a statin, and what happened if they did. The researchers found that more than 90 percent of patients who stopped taking statins because of an adverse reaction could tolerate the medication when tried again. The study is published in the April 2, 2013 issue of the Annals of Internal Medicine. This was a NIH Challenge Grant, supported by NLM with funds from the American Recovery and Reinvestment Act. (http://www.nlm.nih.gov/ep/awardsarra.html)
Huabing Zhang, MD; Jorge Plutzky, MD; Stephen Skentzos, BA, BS; Fritha Morrison, MPH; Perry Mar, PhD; Maria Shubina, ScD; and Alexander Turchin, MD, MS. Discontinuation of Statins in Routine Care Settings: A Cohort Study Ann Intern Med. 2 April 2013;158(7):526-534
From NLM News & Events
NLM-Funded Study Offers New Insight into Statin Discontinuation
Media Reports about this Study
Understanding Statin Discontinuation, Bringham and Women’s Hospital News
Dr. Kwangsik Nho received the Silver Medallion Poster at the 2013 AMIA’s Summit on Translational Bioinformatics Conference (TBI). Dr. Nho is a current K99 award recipient and a former trainee of the NLM University-based Biomedical Informatics Research Training Program at University of Virginia. His poster entitled “Integration of bioinformatics and imaging informatics for identifying variants from whole-exome sequencing” was selected as top poster in the “Silver Medallion” poster framing session. Using a novel strategy to combine bioinformatics and imaging informatics methodologies, he performed whole-exome sequencing to identify functional non-synonymous variants in protein-coding regions associated with atrophy rate of hippocampal volume. This analysis of longitudinal change in hippocampal volume through an extreme-trait whole-exome sequencing design with extension via meta-analysis is considered as one of the first of its kind.
Dr. Peter Szolovits, Professor of Computer Science and Engineering and head of the Clinical Decision-Making Group at MIT, is the 2013 recipient of the Morris F. Collen Award of Excellence. The award is given in honor of Morris F. Collen, a pioneer in the field. The award is the highest honor in informatics that is presented by the American College of Medical Informatics to an individual whose personal commitment and dedication to biomedical informatics has made a lasting impression on healthcare and biomedicine. Dr. Szolovits’ research centers on the application of Artificial Intelligence methods to problems of medical decision making and design of information systems for health care institutions and patients. He has worked on problems of diagnosis, therapy planning, execution and monitoring for various medical conditions, computational aspects of genetic counseling, controlled sharing of health information, and privacy and confidentiality issues in medical record systems.
Dr. Joshua Denny received the American Medical Informatics Association (AMIA) New Investigator Award at the association’s 2012 Annual Symposium recently. The award is given in recognition of an individual for early informatics contributions and significant scholarly contributions on the basis of scientific merit and research excellence. Nomination criteria for the award include significant scientific productivity in informatics prior to eligibility for fellowship in the College of Informatics, multiple significant scientific publications, and demonstrated commitment to AMIA. Dr. Denny’s primary research focuses on developing methods to identify phenotypes from electronic health records (EHR), performing genomic and pharmacogenomics analyses using EHR-linked genomic data, and creating the resources needed to translate this knowledge into clinical practice. His research is supported in part by NLM Grant R01LM010685.
Dr. George Hripcsak is one of the new members announced by the Institute of Medicine (IOM) during its 42nd annual meeting.
IOM News Release: http://www.iom.edu/Global/News%20Announcements/2012-New-Members.aspx
George Hripcsak, MD, MS, is Vivian Beaumont Allen Professor and Chair of Columbia University’s Department of Biomedical Informatics and Director of Medical Informatics Services for New York-Presbyterian Hospital. Dr. Hripcsak is a board-certified internist with degrees in chemistry, medicine, and biostatistics. Dr. Hripcsak’s current research focus is on the clinical information stored in electronic health records. Using data mining techniques such as machine learning and natural language processing, he is developing the methods necessary to support clinical research and patient safety initiatives. He is currently co-chair of the Meaningful Use Workgroup of HHS’s Office of the National Coordinator of Health Information Technology; it defines the criteria by which health care providers collect incentives for using electronic health records. Dr. Hripcsak was elected fellow of the American College of Medical Informatics in 1995 and served on the Board of Directors of the American Medical Informatics Association (AMIA). Dr. Hripcsak has been an NLM R01 grantee for over a decade and he is the Director of the NLM-funded Biomedical Informatics training program at Columbia. Dr. Hripcsak is a former chair of NLM’s Biomedical Library and Informatics Review Committee (BLIRC), and he is a fellow of the New York Academy of Medicine. He has published over 200 papers.
George M. Hripcsak, MD, MS
Discovering and Applying Knowledge in Clinical Databases
5 R01 LM006910-13
Columbia University Health Sciences
"Screening nonrandomized studies for medical systematic reviews: A comparative study of classifiers." Artificial Intelligence in Medicine: http://www.aiimjournal.com/article/S0933-3657(12)00062-0/abstract
In this study, Drs. Bekhuis and Demner-Fushman concluded that machine learning classifiers can help identify nonrandomized studies eligible for full-text screening by systematic reviewers. Optimization can markedly improve performance of classifiers. However, generalizability varies with the classifier. The number of citations to screen during a second independent pass through the citations can be substantially reduced.
This work was supported by NLM Grant R00LM010943.
"A Whole-Cell Computational Model Predicts Phenotype from Genotype." Cell: http://www.cell.com/abstract/S0092-8674%2812%2900776-3
Dr. Markus Covert and his research team at Stanford University reported on July 20, 2012, in the journal Cell, on their breakthrough effort of completing the world’s first computational model of an organism. By encompassing the entirety of an organism in silico, the model allow researchers to address questions that aren’t practical to examine otherwise, representing a stepping-stone toward the use of computer-aided design in bioengineering and medicine.
This work was supported by NLM Grant DP1LM011510.
From NLM News & Events
NLM-Funded Investigator Creates First Complete Computerized Simulation of an Organism
Media Reports about this Study
Stanford University News Service: http://news.stanford.edu/pr/2012/pr-computer-model-organism-071812.html
New York Times: http://www.nytimes.com/2012/07/21/science/in-a-first-an-entire-organism-is-simulated-by-software.html?_r=2&ref=science
Jason H. Moore, PhD, a bioinformatics methodologist, is the Third Century Professor and Director of the Institute for Quantitative Biomedical Sciences at Dartmouth College. Dr. Moore’s NLM-funded bioinformatics research program focuses on the development, evaluation and application of computational methods for characterizing gene interactions in studies of common human diseases.
Board of Regents Presentation: "Machine Learning Approaches to the Genetic Analysis of Complex Traits." The sequencing of the human genome has made it possible to identify millions of rare and common variants across the genome that can be used to carry out genome-wide association studies (GWAS). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control and data analysis. This work has been successful and has enabled the discovery of new associations. However, it is now recognized that most SNPs discovered via GWAS have very small effects on disease susceptibility and thus may not be suitable for improving healthcare through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic bioinformatics approach that recognizes the complexity of the genotype-phenotype relationship that is characterized by significant heterogeneity and gene-gene and gene-environment interaction. Machine learning has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases.
This work is supported by NLM grant R01LM009012. An American Recovery and Reinvestment Act (ARRA) Summer Research Experience (SRE) Supplement allowed several high school students to participate in this novel genomic research.
Dr. John H. Holmes, an epidemiologist and medical-information specialist, is Associate Professor of Medical Informatics in Epidemiology at HUP, University of Pennsylvania Perelman SOM. When this study, called MICE for short, is completed, Dr. Holmes's team of researchers will have accessed more than one million message boards and Twitter feeds posted by breast-cancer and prostate-cancer patients who discuss the use and effects of herbal and nutritional supplements. This work is supported by NLM Grant RC1LM10342.
Media Reports about this Study
Wall Street Journal: http://online.wsj.com/article_email/SB10001424052702303404704577309794125038010-lMyQjAxMTAyMDIwMDEyNDAyWj.html?mod=wsj_share_email
"Expression-based genome-wide association study links the receptor CD44 in adipose tissue with type 2 diabetes." Proceedings of the National Academy of Sciences: http://www.pnas.org/content/early/2012/04/10/1114513109.long
In a study published online April 9, Dr. Butte and his team combed through public databases storing huge troves of results from thousands of earlier experiments. They identified a gene never before linked to type-2 diabetes, a life-shortening disease that affects 4 percent of the world’s population. These findings have both diagnostic and therapeutic implications. This work was supported by NLM Grant R01LM9719.
"Extension of RPI-adult male and female computational phantoms to obese patients and a Monte Carlo study of the effect on CT imaging dose." Physics in Medicine & Biology: http://iopscience.iop.org/0031-9155/57/9/2441/article
This study examines the effect of obesity on the calculated radiation dose to organs and tissues from CT using newly developed phantoms (models) representing overweight and obese patients. This set of new obese phantoms can be used in the future to study the optimization of image quality and radiation dose for patients of different weight classifications. This work was supported by NLM Grant R01LM009362.
Media Reports about this Study
RPI News and Events: http://news.rpi.edu/update.do?artcenterkey=3018&setappvar=page(1)
US News & World Report: http://health.usnews.com/health-news/news/articles/2012/04/13/ct-scans-deliver-more-radiation-to-obese-people-study
Dr. Peter J. Embi
Dr. Peter Embi received the inaugural Distinguished Paper Award at the American Medical Informatics Association (AMIA) 2012 Summit on Clinical Research Informatics. He was awarded this for his paper titled: "Evaluating Alert Fatigue and Response Patterns to EHR-based Clinical Trial Alerts: Findings from a Randomized, Controlled Study." The award is given "In recognition of research presented at the AMIA Clinical Research Informatics Summit that contributes to the state of knowledge and practice, is novel, and will impact future work through its dissemination." The full paper will appear in a special issue of the Journal of the American Medical Informatics Association, which will be dedicated to Clinical Research Informatics and is scheduled for publication in June 2012. This work was supported by NLM Grant R01LM009533.