University of Missouri – Kansas City, Atterbury Student Success Center- Pierson Auditorium
5000 Holmes, Kansas City, MO 64110
THURSDAY, APRIL 11, 2019
10:00 AM–10:30 AM
WELCOME AND INTRODUCTIONS
Keith Gary, PhD | Welcome
Mark Hoffman, PhD | Working in the Big Tent of Bioinformatics
10:30 AM – 11:30 AMADI
Wendy Chapman, PhD | Informatics as a Journey: How Your Expertise is Needed in the Grand Goal of Transforming Healthcare
Informatics is critical to the transformation of healthcare as we seek to increase patient safety, catalyze new discoveries, and improve clinician and patient experience. As a multi-disciplinary field, it is clear how training in computer science and engineering, health, and biostatistics contribute to this grand cause. But to be effective, informatics also needs contributions from many other fields such as psychology, human-computer interaction, linguistics, change management, and sociology. Dr. Chapman will discuss the need for multi-disciplinary contributions to informatics by describing her journey to this career as well as by sharing stories from other unlikely contributors to the field.
11:30 AM–1:00 PM
LUNCH & POSTER PRESENTATION
1:00 PM – 2:00 PM
Carolyn Lawrence-Dill, PhD | Computing on Biological Data in Plants: From Genome to Phenome (and Beyond)
In my research group we use data science approaches to solve plant biology and crop improvement problems by investigating the structure and function of plant genomes as well as the interrelationships among genomes, environments, and phenotypes. Our work has focused on mapping genomes and gene elements, predicting gene function, inventing new ways to link genes to phenotypic descriptions and images, developing ways to compute on phenotypic descriptions, organizing broad datasets for community access and use, and developing computational tools that enable others to do all of these sorts of analyses directly (https://dill-picl.org/projects/). These sorts of projects require not only a background in biology and computer science, but also an understanding of human/computer interaction, natural language processing, and engineering principles. As such, we work with psychologists, sociologists, linguists, engineers and others. If time allows, I will describe some challenges and opportunities that working across disciplines can bring.
Stephen Simon, PhD | Mining the Electronic Health Record
The electronic health record (EHR) offers opportunities for research and quality improvement studies that did not exist before. Data mining, discovering new and unexpected patterns in the data, requires a different mode of access for EHR data than more traditional hypothesis driven studies. This talk will cover the specialized statistical and programming skills needed for data mining.
Matt Obenhaus | Standards-Based APIs in Healthcare – Where Do We Take It From Here?
Abstract Coming Soon.
2:00 PM – 3:00 PM
DATA STANDARDIZATION AND INTEGRATION
MODERATOR: Susan Brown, PhD
Baek-Young Choi, PhD | Blockchain: Opportunities and Challenges for HealthCare
Blockchain technology has the potential to transform the health care echosystem, significantly increasing the interoperability, security and privacy of health records. However, the technology is not fully mature yet, nor can be immediately applied for arbitrary systems. The capabilities and limitations of blockchain for healthcare systems will be summarized. Some operational, organizational, and societal issues in adoption of blockchain in health care systems will be also pointed out.
Martina Clarke, PhD | Increasing the Use of Personal Health Record Through User-Centered Design
The Personal Health Record (PHR) is intended to support patients’ access to data, clinical summaries, preventive care, educational materials, and medication reconciliation. It is one of the core requirements for Meaningful Use Stage 3. The PHR aims to improve medication adherence, self-management of disease, and the patient provider communication. Despite the potential benefits of PHRs, adoption has been poor, in part due to usability issues. While 75% of patients see value in a PHR, fewer than 10% are users. Current research focuses heavily on improving PHR usability to increase use, without taking into consideration patient-specific factors that affect PHR adoption. By understanding the needs of users, the use and utility of the PHR will increase.
Guoqin Yu, PhD | Microbiome and Cancer Epidemiology
Human microbiota has been shown to play a critical role in human health. Studies on human microbiota is exploring. However, quality microbiota data collection and analysis are challenging. The techniques for microbiota data collection and analysis will be discussed, especially in the perspective of epidemiology study.
3:00 PM – 3:30 PM
3:30 PM – 4:30 PM
MODERATOR: Trupti Joshi, MBBS, ADB, MS, PhD
James Miller, PhD | Visualization in the Arts and Sciences
Visualization could be very simply defined as the use of interactive computer graphics to present data and provide insight into that data to stakeholders. In recent years, the use of visualization has become much more than simple after-the-fact presentation of results. Many researchers have come to realize that visualization is actually a powerful tool used throughout the scientific discovery process. In addition, the idea that visualization can be used as a powerful form of storytelling has emerged as an important area of research. In this presentation, I will briefly highlight a bit of what is known and not known from a theoretical perspective, and then present results from some recent projects.
Jay Unruh, PhD | Single Particle Averaging Super-Resolution Mapping of Macromolecular Complexes in Situ
The organization of megadalton complexes in living organisms represents a challenge that has been addressed in the past by Cryo-EM, X-ray, and computational modeling. These approaches are expensive and struggle to provide the throughput or context necessary for large scale mapping of cellular organization and heterogeneity. With the advent of super-resolution fluorescence microscopy, we can now resolve large scale features of such structures. We have demonstrated in situ single particle averaging methodologies to map out the organization of the yeast cell division machinery and the fruit fly synaptonemal complex. In yeast, this has allowed us to quantify novel interactions necessary for insertion of the centrosome as well as the asymmetry that orients its duplication. It is our hope that this methodology will greatly speed the progress of large scale structural modeling in many organisms.
Thomas Coffin | Experiencing Data through Interactive Visual Explorations
We are experiencing an exponential growth of data in all aspects of human life to the point that the vast amounts of data are becoming overwhelming to manage as well as are starting to be unused due to our lack of tools to extract meaningful information from the raw data. Social media, advances in sensors, new computational models, web surveys, electronic transactions, are just examples of data generation/collection technologies that are capturing much more than we can handle with the current approaches for data analysis. Clearly the human cognitive system only enable us to scrutinize and analyze a limited amount of the raw data we generate, and therefore limiting also the quality of our scientific insight on the problem at hand. Consequently, our data-rich world is developing a critical need for visualization as a key component of the scientists’ tool set for discovery and insight into their areas of expertise.
Visualization, and especially interactive visualization, takes advantage of the bandwidth of the human visual system, our ability to visually identify patterns and relationships, and how we interact with the data to extract information. This presentation explores the power of visualization to extract information from big data by presenting an introduction to visualization, to current methods and techniques and some illustrative examples of work being done at the Emerging Analytics Center at the University of Arkansas at Little Rock. The presentation seeks to stimulate the audience’s imagination about what’s possible as well as to pursue future research with a multidisciplinary approach in which visualization takes as much as a central role as the data gathering approaches model and analyze a wide variety of problems, phenomena, situations, training and other disciplines of human life.
4:30 PM – 5:30 PM
MODERATOR: Donna Buchannn, PhD
Kathryn Cooper, PhD | Dynamic and Reproducible Network-Based Approaches for Analysis of Temporal Gene Expression
Rooted in graph theory and social sciences, network-based analysis of systems-level molecular data took root approximately 20 years ago. The flexibility and aesthetic appeal of the network model has made it a popular tool for visualization, analysis, and representation of biological variables and their relationships, be these protein-protein interactions, genetic interactions, metabolomic pathways, or patient-caregiver relationships. Network analysis provides a broad-view perspective for datasets with “volume” and “velocity” that have arrived with dropping costs of NGS technology. Our research focuses on temporal analysis of gene expression and NGS data using network modeling and the reliability of these models from one dataset to another. Comparison of systems-level molecular data across the short or long term reveals insights not available using traditional methods, including the formation, maintenance, or loss of critical relationships that sustain cellular functions. In establishing the benchmarks necessary for robust analysis of temporal gene co-expression network analysis, we are also to fill the need for efficient analytical tools that can reliably support clinical decision making and offer insights into chronic disease.
John Symons | Resilience as a Systems-Level Property
Resilience has traditionally been explored from a network science or graph theoretic perspective but social and normative features are often centrally important to the resilience of vital systems. This talk explores how social norms emerge and how they can contribute to resilience.
Keith Slotkin, PhD | Mining Junk for Gold: Reuse of Filtered-Out Reads Provides a New Layer of Epigenetic Data
Deep sequencing datasets undergo filtration steps whereby sequenced reads that do not match the reference genome are discarded. We have gone back to these discarded reads and used a split-read mapping approach to remap the genomic variation in datasets compared to the reference genome. We are not detecting SNP variation, but rather insertion/deletion structural variation, and in particular the new insertion sites of genomic parasites called transposable elements. In addition to genome resequencing data, we can investigate nearly all deep sequencing experimental data types (RNA-seq, ChIP-seq, Methylome-seq, etc…) to identify both small-scale single cases of insertion and large-scale measurements of transposable element activity. Since transposable elements are well-established to be epigenetically repressed in eukaryotic cells, we now have the ability to use virtually any existing sequencing dataset to quantifiably assay the potency of epigenetic regulation in that sample, using transposable element activity as the barometer.
Michael Nassif, MD | Title Coming Soon
Abstract Coming Soon
5:30 PM – 8:00PM
COCKTAILS, NETWORKING, & DINNER
FRIDAY, APRIL 12, 2019
7:15 AM–7:45 AM
7:45 AM–8:00 AM
WELCOME & INTRODUCTIONS
Keith Gary, PhD | Thank Sponsors and Volunteers
Mark Hoffman, PhD | Welcomead
8:00 AM – 9:00 AM
Scott Shearer, PhD, PE | Digital Agriculture – Disruption or Distraction?
Many data scientists argue that machine and agronomic data in agriculture fail to meet the definition of “big data.” However, the combination of technology and venture capital directed at agriculture are changing the landscape with respect to on-farm production. The roots of the digital revolution in agriculture began with the advent of GNSS and precision agriculture in the mid 1990s. Couple rapid advancement in technologies (i.e., wireless communications, unmanned aerial systems, robotics and cloud computing), a boom cycle in agriculture (2008-2015) and talk about feeding 10 billion people by 2050; and many investors and businesses now view agriculture as the new frontier. This presentation will overview the evolution of what many are calling digital agriculture – connecting the farm to the internet. Topics shaping the future of agriculture to be explored will include cloud computing, Internet of Things, data standards and exchange, federated IDs, blockchains, data ownership, data privacy and security, emerging ecosystems, broadband internet access and supervised autonomy. The adoption of technology in agriculture is proceeding at a rapid pace causing concern on the part of farmers and traditional agribusinesses. This presentation will provide an overview of the forces shaping the future of agriculture and some of the risks faced by those who fail to prepare for this digital revolution.
9:00 AM – 10:00 AM
MODERATOR: Ryan Moog
Douglas Marthaler, PhD | Bayesian and Bioimmunoinformatic Methods to Understand Rotavirus Genetics and Antigenicity
Our research group studies the emergence and transmission of animal viruses, aiming to understand the molecular determinants of antigenicity to protect animal and public health. Extensively research is being conducted on rotaviruses, which are a significant cause of enteric disease in many animals, including humans. While the human rotavirus A vaccine has prevented millions of infant deaths, rotavirus C has been established as a cause of neonatal piglet mortality, leading us to determine the evolutionary relationships among rotavirus C strains from multiple hosts and investigate potential zoonotic transmission. Additionally, we are using in silico methods for elucidating putative B cell epitopes on the rotavirus C virion for the development of second generation vaccine technologies for this fastidious pathogen.
Kim Smolderen, PhD | Applying a Random Forest Algorithm to Build a Quality of Life Prediction Model for Peripheral Artery Disease: A Simple Application of a Machine Learning Algorithm
Patients with peripheral arterial disease (PAD) have a number of potential therapeutic options however, it is currently unknown how their personal characteristics and different treatments might impact their health status (symptoms, function and quality of life). The first step in creating personalized estimates for patients is to model 1-year health status outcomes. A wealth of information was collected on 1275 patients with PAD that were enrolled in the international PORTRAIT registry. To weigh the relative importance of clinical and patient characteristics for 1-year quality of life outcomes, we explored whether a simple application of a supervised machine learning algorithm – a random forest algorithm – would be useful to help select variables to build our quality of life model. A real-world example using the PORTRAIT observational study data will be provided.
Henry Yeh, PhD | Brain Age Gap Estimate (BrainAGE): A Reliable Brain Health Measure
Despite the guidelines of Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification Diseases (ICD), diagnosis of mental health remains challenging, mainly due to lack of objective and reliable criteria. For decades, the psychiatry community have been looking for objective measures that can be used as diagnosis tools and/or biomarkers for treatments but have only found few. Recently, multiple labs have been able to demonstrate reliable predictions on chronological age using brain imaging techniques and proposed the Brain Age Gap Estimate (BrainAGE), defined as the difference between estimated and chronological age, as a brain health measure. In this talk, I will present estimation of BrainAGE using Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), and an application in randomized clinical trial for ibuprofen.
Adina Howe, PhD | Digging in Dirt: Finding Signal From Noise
High throughput sequencing platforms have rapidly changed the quantity of data that can be used to explore natural environmental systems. In the GERMS lab, we integrate these technologies to understand how we can better manage our land and water resources. A constant challenge is distinguishing single from noise, especially in very complex and heterogeneous environments. I’ll present some of the challenges, solutions, and impact of using data analysis in two different systems, beneficial and harmful microbial interactions in agricultural soils and the development of harmful algal blooms in Iowa freshwater.
Abstract Coming Soon
10:00 AM – 10:30 AM
10:30 AM–11:30 AM
MODERATOR: Devin Koestler, PhD
Doina Caragea, PhD | Root Anatomy Based on Root Cross-Section Image Analysis with Deep Learning
The aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has the potential to further increase food production. However, the belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images. Using the Faster Region-based Convolutional Neural Network (Faster R-CNN), the DL-RootAnatomy models detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes are used to estimate the root diameter, stele diameter, and late metaxylem number and average diameter. Experimental evaluation has shown that our models can accurately detect the root, stele and late metaxylem objects, as well as their anatomical traits.
Andres Bur, MD | Clinical Applications of Deep Neural Network-Based Image Classification in Oncology
Deep neural networks (DNN) are a subset of machine learning that are well-suited for complex image classification tasks and form the basis for facial recognition and image search technologies. DNN have been used in oncology research to automate analysis of cross-sectional imaging, histopathologic slides and clinical photos. Deep learning has the potential to support physicians by enhancing human performance in tedious tasks, in which computers excel. Additionally, deep learning can enhance personalization of oncology care. This presentation will review current and future applications of DNN for automated image classification that will transform cancer care.
Sean McKinney | Convolutional Neural Networks as an Image Processing Swiss Army Knife
Convolutional neural networks have proven to be capable of performing a diverse range of image processing tasks. Our team has applied them to over a dozen tasks spanning 2D/3D nuclear segmentation, automated clustering of image cytometry data, spot identification, yeast segmentation, and electron microscopy segmentation. All of these were tasks that were difficult if not impossible to reliably automate using traditional processing methods and were accomplished with the same underlying architecture in a relatively brief span of time.
11:30 AM–12:30 PM
MODERATOR: Mark Nichols, PhD
Mark Clements, MD, PhD | Transforming Type 1 Diabetes Care with Advanced Machine Learning and Quality Improvement Methods
Inadequate blood glucose control in type 1 diabetes significantly increases lifelong risk for cardiovascular complications and for mortality. Most youth with type 1 diabetes are not achieving targets for blood glucose control; approximately 18-20% will experience deterioration in control from age 8-6 years. The ability to predict youth who will experience a rise in hemoglobin A1c, the major biomarker of blood glucose control, would allow clinicians to use more intensive management approaches to reduce the deterioration in blood glucose control. Combining predictive analytics with quality improvement methodologies can help clinicians rapidly identify alternate management approaches that work in youth at risk for deteriorating disease control. This approach can be applied across most chronic diseases.
Jonathan Mitchem, MD | Overcoming Resistance to Immune Based in Colorectal Cancer
Immune based therapies, such as immune checkpoint blockade, have revolutionized the treatment of some difficult to treat malignancies. These results, however, have not translated to the majority of patients with colorectal cancer despite a preponderance of data suggesting anti-tumor immunity is important for treatment response, recurrence, and survival in this disease. Immune based therapy should work in colorectal cancer, but currently it does not. To tap into the potential of immune based therapy in colorectal cancer, we must understand why this therapy works in some patients and how currently utilized therapy alters anti-tumor immunity. Answering these questions will help us to devise better therapeutic strategies and then chose the right patients for that treatment.
Suzanne Arnold, MD, MHA | Improving Decision Making in Elderly Patients with Valvular Heart Disease
Valvular heart disease is incredibly common in elderly people and impacts both survival and quality of life. Both aortic stenosis and mitral regurgitation have traditionally required open-heart surgery to correct. Over the past 10-20 years, transcatheter procedures have been integrated into our treatment options to allow us to treat these valvular conditions less invasively. Given the advanced age of the patients who are candidates for these procedures and their burden of comorbidities, the decision as to whether or not to proceed with an invasive procedure (albeit less invasive than surgery) can be challenging. I will discuss work done to try to define the potential benefit of these procedures, to identify the patients most (or least) likely to benefit, and how this can be used to better inform these treatment decisions.
Kari Lane, PhD, RN, MOT | Utilizing AI and Embedded Sensors to Provide Early Alerts for Health Changes in Older Adults
TigerPlace is an independent living facility that was built and licensed to nursing home standards. TigerPlace started caring for residents in 2004 and installed embedded sensors in approximately 50% of resident apartments (as they consented) since that time. This sensor system has detected changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Over the past 14 years, we have monitored both the residents with sensors and those without sensors while providing interdisciplinary care coordination to all residents. We have a proactive model. We highly encourage active engagement and mobility while providing an Aging in Place atmosphere. We have collected continuous data 24 hours/7 days a week from motion sensors to measure overall activity, an under-mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns. We have tracked multiple health outcomes which demonstrate a decline in cognition and mobility over time in both groups, and better quality of life outcomes including (ADLs, incontinence, falls, depression, social and mental functioning, and adjustment). Our findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.