Poster Abstracts

Poster #1: An Oncospace Risk Prediction Model for Head and Neck Radiation Toxicities: Novel Insights to Reduce the Risk of Head and Neck Radiation-Induced Xerostomia

Xuan Hui, Harry Quon, Scott P. Robertson, Zhi Cheng, Joseph A. Moore, Michael R. Bowers, Brandi R. Page, Ana P. Kiess, Minoru Nakatsugawa, Seyoun Park, Junghoon Lee, Todd R. McNutt, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University
Purpose: Risk of late normal tissue toxicities such as xerostomia place significant quality of life and economic burdens on surviving patients. Preventing this toxicity remains limited. The aim of this study is to build a robust comprehensive xerostomia risk prediction model as the foundation for a personalized learning health system (LHS) by incorporating a wide … Continued

Poster #2: Analysis of Lagrangian stretching in turbulent channel flow using a task-parallel particle tracking algorithm in the Johns Hopkins Turbulence Databases

Perry L. Johnson1, Stephen H. Hamilton2, Randal Burns2, Charles Meneveau1, [1] Department of Mechanical Engineering, Johns Hopkins University, [2] Department of Computer Science, Johns Hopkins University
The exponential deformation of fluid elements along Lagrangian paths is an intrinsic property of turbulent flows with importance in a wide variety of natural and engineering flows, such as droplet or bubble break-up, polymer-induced drag reduction, and device-induced hemolysis. The production of enstrophy by vorticity stretching, a dynamically important process in the turbulent energy cascade, … Continued

Poster #3: Citizen Social Science

Daniel Darg1, Alex, Szalay1, 2, M. Jordan Raddick1, [1] Institute for Data Intensive Engineering and Science, Krieger School of Arts & Sciences, Johns Hopkins University; [2] Department of Computer Science, Whiting School of Engineering, Johns Hopkins University
Citizen Social Science (CSS) is project centered on the development of a research and social-media platform that enables anyone to carry out social-scientific investigations. It aims to make survey research fun and economical by letting the crowd determine what questions or pattern-recognition tasks get presented to the crowd, and by letting the crowd in turn … Continued

Poster #4: Data-driven Quality Improvement: The Case of Precise Blood Pressure Measurement

Nikita Stempniewicz1, 2, Elizabeth Ciemins2, Cindy Shekailo2, and John Cuddeback2 , [1] Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, [2] AMGA Analytics
Research Objectives: To determine the degree of rounding in blood pressure (BP) recordings occurring at 16 multi-specialty medical groups and integrated delivery systems across the U.S., share findings, track changes over time, and identify organizations with the largest improvements in the precision of BP recording to better understand drivers of these improvements. Study Design: Blood … Continued

Poster #5: Decision Tree Model to Predict Weight Loss Following Radiotherapy in Head and Neck Cancer Patients

Zhi Cheng MD, MPH1, Minoru Nakatsugawa PhD1, 2, Chen Hu PhD3, Ana P. Kiess MD, PhD1, Scott P. Robertson PhD1, Joseph A. Moore PhD1, Michael R. Bowers BS1, Xuan Hui MD, MS1, Brandi R.Page MD1, Laura Burns BSN1, Mariah MuseBSN1, Amanda Choflet MS, RN, OCN 1, Kousuke Sakaue4, Shinya Sugiyama4, Kazuki Utsunomiya4, John W. Wong PhD1 , Todd R. McNutt PhD1, and Harry Quon MD, MS1, [1] Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, [2] Toshiba America Research, Inc., Baltimore, MD, [3] Oncology Center – Biostatistics/Bioinformatics, Johns Hopkins University, Baltimore, MD, [4] Toshiba Medical Systems Corporation, Otawara, Japan
Abstract: Background and Purpose: To evaluate a prediction model of weight loss in head and neck cancer (HNC) patients treated with radiotherapy (RT) by Classification and Regression Tree (CART) algorithm as a component of learning health system (LHS). Material and Methods: From a prospectively collected database, 391 HNC patients from 2007 to 2015 were identified. … Continued

Poster #6: SciServer Compute Bringing Analysis Close to the Data

Jai-Won Kim*, Gerard Lemson, Institute for Data Intensive Engineering and Science, Krieger School of Arts & Sciences, Johns Hopkins University
SciServer Compute is a recent addition to SciServer, a Big Data infrastructure project developed at Johns Hopkins University that provides a common environment for sharable, computationally-intensive research. SciServer Compute implements Jupyter notebooks in Docker containers to bring advanced analysis capabilities close to Terabyte-scale relational databases and Petabyte-scale file storage systems. In addition to real-time analysis … Continued

Poster #7: StereoGene: Rapid Estimation of Genomewide Correlation of Continuous or Interval Feature Data

Elena D. Stavrovskaya 1,2, Alexander V. Favorov*3,4,5, Tejasvi Niranjan6, Sarah J. Wheelan6 and Andrey Mironov 1,2, [1] Dept. of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia, [2] Institute for Information Transmission Problems, RAS, Moscow, Russia, [3] Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University, Baltimore, MD, [4] Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow, Russia, [5] Laboratory of Bioinformatics, Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia, [6] Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine
Motivation: High throughput sequencing methods produce massive amounts of data. The most common first step in interpretation of these data is to map the data to genomic intervals and then overlap with genome annotations. A major interest in computational genomics is spatial genome-wide correlation among genomic features (e.g. between transcription and histone modification). The key … Continued

Poster #8: Teaser: comprehensive read mapper benchmarking in 20 minutes for genomes, transcriptomes, methylomes and metagenomes

Moritz G. Smolka1, Florian Breitwieser2, Steven L. Salzberg2, 3, 4, Arndt von Haeseler1, Michael C. Schatz3, Fritz J. Sedlazeck3, [1] Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, Vienna, Vienna, Austria; [2] Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University; [3] Department of Computer Science, Johns Hopkins University; [4] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland USA
In recent years over 100 read mappers have been published to analyze high throughput sequencing data, each of which is optimized for different assays or requirements. The large number of potential mappers and the even larger number of possible parameter settings make it challenging to choose the most appropriate mapper for a given experiment. Consequently … Continued

Poster #9: Video Representation: More than Feature Mean

Xiang Xiang, Department of Computer Science, Whiting School of Engineering, Johns Hopkins University
We need the mean and variation to represent a data distribution while the mean itself is not a robust statistic. However, feature averaging is straightforward and conventional to represent a sequence such as in the recent works of video captioning and activity recognition. We argue that the framewise feature mean is unable to characterize the … Continued