Winners Announced for the 2016 DCEG Informatics Tool Challenge
, by DCEG Staff
Eight winners of the 2016 DCEG Informatics Tool Challenge were announced in June. Since 2014, the competitive funding challenge has provided support for innovative approaches to further enhance DCEG’s epidemiological methods, data collection, analysis, and other research efforts through the use of modern technology and informatics.
Proposals were evaluated for their innovative use of current technologies to address a specific research need, the ability for the project to be completed within one year of initiation, and the cost, which was not to exceed $20,000. Reviewers considered the utility to epidemiologic and genetic research and technical feasibility of each proposal.
NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) is providing technical support for three of the projects, NIH’s Center for Information Technology (CIT) is collaborating on one project, contractors from Information Management Services, Inc. (IMS) are providing support for two others, and researchers from George Washington University and The Ohio State University are collaborating on another. Staff members from the NCI Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) are working with DCEG researchers on two of the projects.
Out of twelve submitted proposals, the eight described below were selected for funding.
An electronic platform for informed consent (eConsent)
This web-based tool will electronically obtain research consent from individuals who wish to enroll in epidemiological and/or clinical research studies. Using an interactive approach, this tool will be developed using responsive design method which provides participants the convenience of accessing consent documentation on their computer, smart phone, or tablet, and allowing researchers to manage paperless consent records efficiently.
SOCcer 2.0: Improving the classifier performance in SOCcer
Melissa Friesen (DCEG); Daniel Russ, Calvin Johnson (NIH/CIT); Carl McCabe, Sue Pan (CBIIT)
To facilitate the incorporation of occupational risk factors into large-scale epidemiological studies, DCEG recently developed the SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) algorithm, which automatically assigns standardized occupation classification codes based on job descriptions. SOCcer 2.0 improves upon the original algorithm’s ability to efficiently identify, classify, and code occupations and estimate occupational exposure measurements by better capturing tasks associated with the SOCcer-generated standardized occupational codes. These efforts also build upon the 2015 DCEG Informatics Tool Challenge award for companion software SOCAssign. Read more about SOCcer in the spring 2016 issue of DCEG Linkage.
A web tool for estimating absolute and relative risk for cohorts assembled from electronic health records within health care systems
Epidemiologic research is based increasingly on observational data from routine clinical care captured in electronic medical records, but this presents many challenges for analysis. This web tool and customized R software package will use a new class of models called “prevalence-incidence mixture models” to support analyses of cohort data assembled within health care systems. These models seek to address the potential need for absolute and relative risk estimation for screen-detected cancer and precursors.
The right questions at the right time: Using smartphones to integrate real-time environmental sensor data and survey responses
This first-of-its-kind particle sensor for personal and research use will remotely collect contextual information on the end users’ activities that coincide with changes in concentrations of ambient PM2.5, a breathable particle linked to the development of chronic respiratory conditions, cardiovascular and respiratory mortality, and lung cancer. By integrating two existing systems—a real-time, Bluetooth-enabled monitor that measures fine particulate matter and PiLR Health, a cloud-based study management system with a smartphone application—researchers will incorporate use of the sensor data into existing and future studies of indoor and outdoor air pollution in China.
LDLink feature expansion, maintenance and new association module
Mitchell Machiela (DCEG)
Researchers will expand on the current success of LDlink, a publicly accessible web tool that easily and efficiently explores regional linkage disequilibrium (LD) and links correlated alleles for functional studies. By adding user requested options, a new module for visualizing and interactively exploring association results from genome-wide association studies (GWAS), designating resources to maintain LDLink, and fixing reported bugs, this award will enrich LDLink into a more comprehensive toolbox for investigating LD.
COMETS-Analytics: A data analysis platform for multi-cohort metabolomics analyses
The Consortium of Metabolomics Studies (COMETS) comprises 27 international prospective cohorts and cohort consortia using metabolomics in studies of disease etiology. Investigators are collaborating with CBIIT to create two publicly accessible data analysis applications for metabolomics datasets—a metabolite harmonization app and a correlational analysis app. These apps will feature a streamlined user interface to make them easier and more efficient for COMETS investigators and cohorts to use. The ultimate goal is to improve scalability, sustainability, and reproducibility of metabolomics research.
An enhanced web tool for conducting pathway meta-analysis using summary data from GWAS
This easy-to-use web tool was initially funded by the 2015 DCEG Informatics Tool Challenge award. It allows a wider range of researchers to explore various disease-pathway associations using summary results from GWAS. This enhancement will allow for a more flexible and user-friendly interface.
TWAS: A webtool to explore TCGA data for cancer post-GWAS study
TWAS is a suite of web-based applications designed to easily and efficiently explore variant-based information for post-GWAS study. This webtool explores The Cancer Genome Atlas (TCGA) data and conducts eQTL, mQTL, and ASE analyses of various cancer types. TWAS will use TCGA data to help researchers identify target genes of cancer risk loci identified in GWAS.