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Winners Announced for the 2018 DCEG Informatics Tool Challenge

, by DCEG Staff

In June, DCEG Director Stephen J. Chanock, M.D., announced six winners of the 2018 DCEG Informatics Tool Challenge, a competitive funding program that supports innovative approaches to enhance epidemiological methods, data collection, analysis, and other research efforts of the Division using modern technology and informatics.

Proposals were evaluated for their novel approach to specific research needs, ability for the project to be completed within one year of initiation, and cost, not to exceed $20,000. Reviewers considered the utility to epidemiologic and genetic research as well as technical feasibility.

Six proposals were submitted this year; all were funded. The NCI Center for Biomedical Informatics and Information Technology (CBIIT) is providing technical support; contractors from Information Management Services, Inc. (IMS) are collaborating on two projects;  researchers from the George Washington University and Ohio State University, as well as staff from the NCI Division of Cancer Control and Population Sciences (DCCPS), are collaborating on another.

Online Design Calculator for a Longitudinal Cohort Study

Sung Duk Kim, Ruth M. Pfeiffer, David Check, Paul S. Albert (DCEG)

This R-based, online calculator will support researchers as they design and implement the next-generation cohort, a large, prospective study designed to evaluate how carcinogenic changes unfold over time, resulting in a better understanding of cancer etiology that will help researchers develop more effective strategies for cancer screening and early diagnosis. The tool will estimate the annual resource burden required for a cohort of a given size and given schedule of longitudinal measurements, including: 1) the expected number of cancer cases over a fixed follow-up period; 2) the average number of visits/samples available before a cancer event; and 3) the power to detect associations for longitudinal designs under different follow-up schedules.

A Cloud-Based Webtool for Data Analysis and Visualization for Microbiome Studies

Jianxin Shi, Danping Liu, Paul S. Albert, Christian C. Abnet (DCEG); Sue Pan (CBIIT); William Wheeler (IMS)

DCEG researchers increasingly take advantage of high-throughput sequencing to perform large-scale epidemiologic studies of the human microbiome. As part of these studies, statisticians are faced with performing extensive analyses and generating publishable figures, which can be time-consuming and costly. These challenge winners propose to develop an easy-to-use cloud-based webtool for statistical analyses and data visualization, enabling epidemiologists to perform a large variety of analyses directly and generate high-quality figures for publication.

AuthorArranger: Create Journal Title Pages in Seconds

Mitchell Machiela, Geoffrey Tobias (DCEG)

In the population sciences, it is common for large studies to have hundreds of contributors, often with multiple affiliations. This poses a challenge when submitting manuscripts to journals, because the title page must contain the author names, titles, and affiliations arranged in order of contribution and formatted according to the specific journal’s style—a time- and often resource-consuming endeavor. AuthorArranger is an online tool that aims to make title page creation fast and painless. Users simply upload a file of author names and affiliations, configure formats using simple drop-down menus, preview the title page and make any necessary adjustments, and download the finished title page, ready for submission.

Radiation Dose Calculation Program for Patients Undergoing Nuclear Medicine Procedures: NCINM

Daphnée Villoing, Choonsik Lee (DCEG)

Nuclear medicine (NM) dosimetry is a critical input for epidemiological studies of patients undergoing NM procedures. To improve dosimetry quality for use in nuclear medicine studies, the challenge winners are developing a novel radiation dose calculation program, called the National Cancer Institute dosimetry system for Nuclear Medicine (NCINM). Development will be done in two steps: first, the creation of stand-alone computer software (NCINM); second, the translation of this stand-alone version into a web-based dose calculator. The new dosimetry system will incorporate the NCI’s library of advanced computational human phantoms, which have more realistic anatomy than those used in existing methods. While this new tool will be important for the success of current and future studies in the Radiation Epidemiology Branch, its translation to an online version will make it more attractive and easier to use by medical physicists and nuclear medicine specialists in clinical settings.

Automated metabolite name-harmonization for multi-cohort metabolomics analyses in COMETS-Analytics

Steven C. Moore, Joshua N. Sampson, (DCEG); Krista A. Zanetti (DCCPS); Sue Pan (CBIIT); Ella Temprosa (George Washington University); Ewy Mathé (Ohio State University)

This project focuses on adding metabolite-naming harmonization to the Consortium of Metabolomics Studies (COMETS) online application, named COMETS-Analytics, which was previously developed as a collaboration tool for analysis and sharing data among members of the consortium. Because metabolomics is a new field, and metabolite-naming conventions vary by continent, lab, and cohort, this aspect of the app has continued to be managed by hand. The latest proposed addition to the COMETS app will implement a hierarchical algorithm for harmonizing metabolite names across cohorts, enabling automatic harmonization of names.

A webtool for conducting Mendelian randomization analysis using GWAS summary data

Han Zhang, Kai Yu (DCEG); Bill Wheeler (IMS); Sue Pan (CBIIT)

Development of a user-friendly R package and accompanying web-based interface for non-R users that researchers can use to test whether a risk factor, which is partially determined by a set of genetic markers, has a causal effect on a cancer outcome. The method is based on Mendelian randomization analysis using summary data from genome-wide association studies (GWAS) conducted with a case-control design.

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