Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies

Description

In this episode, Allissa Dillman chats with Margaret Taub, a Senior Scientist with the Johns Hopkins Bloomberg School of Public Health on her journey from mathematics to statistical genetics, highlighting her current role with the Acute to Chronic Pain Signatures (A2CPS) program. The A2CPS study aims to develop predictive biomarkers for chronic pain by collecting comprehensive genomic, psychosocial, and functional data from patients. To coordinate diverse data sources, Margaret emphasized the importance of collaboration and interdisciplinary teamwork, drawing parallels between her experiences in teaching, music, and Ultimate Frisbee to her approach in science. Margaret and Allissa also discuss the study's progress and future plans.

Guest Bio

Margaret Taub, PhD, is a Senior Scientist at Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health. She works collaboratively with clinicians and epidemiologists to use genetic and genomic data to understand the causes of complex diseases. She is also an Investigator with the Data Integration and Resource Center for the Acute to Chronic Pain Signatures (A2CPS) program.

Description

In this episode, Allissa Dillman talks with John Kwagyan about the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in genomics and personalized medicine as well as the promise of these technologies in analyzing complex biological data to advance disease prediction, prevention, and personalized treatments. They also discuss machine learning models, the differences between machine learning and statistical learning, explainable AI, and ethical considerations, as well as the skills future researchers will need to thrive in the AI-genomics landscape.

Guest Bio

John Kwagyan, PhD, is a Statistician and Graduate Associate Professor of Public Health at Howard University College of Medicine, and serves as co-Director of Biostatistics, Epidemiology and Research Design (BERD) at the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS). He is co-PI of the Public Health Informatics and Technology program for District of Columbia (PHIT4DC), and PI (Data Science Core) of the recently funded Howard-Hopkins Comprehensive Alliance in Cancer Research and Education (H2CARE). His research interests include statistical genetics and predictive modelling of clustered data with applications to clinical and public health outcomes.

Episode 11: Reproducible Voice Data as a Biomarker for AI Models

Description

In this episode, Allissa Dillman chats with David Dorr and Andrea Krussel about their roles in the Bridge2AI (https://bridge2ai.org/) program, which aims to enhance AI readiness by improving data standards and developing machine learning practices. Co-PIs in B2AI Voice (https://b2ai-voice.org/voice-ai-summer-school/), Dorr and Krussel discussed their work on creating reproducible voice data as a biomarker for AI models, including training programs for researchers, successful examples of career development, publication of their competency framework (https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1610253/abstract), and upcoming symposium. You can also find the B2AI Voice Data online (https://docs.b2ai-voice.org/) and an AI curricula (https://instructure.okta.com/app/).

Guest Bios

David Dorr, MD, MS earned his bachelor's degree in economics (with minors in mathematics and psychology) and his MD from Washington University in St. Louis. He then completed his internal medicine residency at Oregon Health & Science University and earned a master's degree in medical informatics and health services administration from the University of Utah. Broadly, Dr. Dorr's interests lie in complex care management, especially for older adults and other at-risk populations, coordination of care, collaborative care, chronic disease management, quality and the requirements of clinical information systems to support these areas. From these interests, he has broadened into clinical information needs, Electronic Health Record (EHR) deployment and Health Information Exchange, as a way to expand systems-based approaches to all of health care. Finally, Dr. Dorr performs evaluations of care management and informatics initiatives using a variety of methodologies. Learn more on Dr. Dorr's OHSU profile (https://www.ohsu.edu/people/david-a-dorr-md-ms).

Andrea Krussel is the Director of Education and Workforce Development in the Office of Health Information and Data Science at WashU Medicine. In this capacity, she oversees the operations, development, and implementation of biostatistics, data science, and biomedical informatics academic programs. Andrea holds a BA in Theatre and an MA in Communication Arts and utilizes her teaching, acting, and science backgrounds to provide science communication courses and workshops for the WashU community. She also creates, leads, and evaluates competency-based workforce development initiatives in AI, informatics, and data science at WashU, nationally, and abroad. Andrea is a PhD student in Education Policy, Organization, and Leadership at the University of Illinois, Urbana-Champaign. Her research focuses on integrating artificial intelligence into medical education to enhance clinical decision-making skills in medical students and trainees while easing the administrative burden of clinical instructors.

Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

Episode 10: Overview of the Common Fund Data Ecosystem and how the Training Center supports the CFDE

Description

The National Institutes of Health Common Fund Data Ecosystem (CFDE) aims to enable broad use of Common Fund data to accelerate discovery. NIH Common Fund programs generate a wide range of diverse and valuable data sets and knowledge designed to be used by the research community. The CFDE aims to facilitate improved discovery, reuse, integration, and analyses of these datasets to form novel hypothesis for accelerating discoveries in biomedical research. ORAU received a contract from the NIH to develop a state-of-the-art training center for users of the CFDE. In this cross-over episode with Further Together: The ORAU Podcast, Allissa Dillman, Ph.D., co-principal investigator and owner of BioData Sage LLC, and Jennifer Burnette, ORAU project director, talk to host Michael Holtz about the importance of the CFDE Training Center and how it can help researchers mine data for omics research of various kinds.

Guest Bios

Allissa Dillman, PhD, co-PI and Training and Engagement Director for the CFDE Training Center, is the founder and CEO of BioData Sage LLC, a company focused on providing a holistic approach to data science integration in the biomedical and biological science fields. She works with clients in industry, academia, government, and nonprofit sector to create and support training programs on bioinformatics, cloud computing, and the tools and standards for reproducible data science practices for scientific and lay communities. She also creates community events, such as hackathons, where broad communities work towards solving real biomedical data challenges.

Jennifer Burnette, MPH, CFDE Training Center Director and project manager and director for ORAU expertly steers complex, interdisciplinary programs that bridge local, state, and federal stakeholders. Her career is a testament to her ability to navigate complex networks, having successfully coordinated projects for over 20 federal agency customers including the Centers for Disease Control and Prevention (CDC), Department of Energy (DOE), Department of Homeland Security (DHS), Federal Emergency Management Agency (FEMA), National Institutes of Health (NIH), National Science Foundation (NSF), and 13 DOE and other federal national laboratories.

Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

Episode 09: MoTrPAC and the Science of Fitness

Description

In this episode, Allissa Dillman chats with researchers Malene Lindholm and Dan Katz about their work on MoTrPAC. Malene has a 20-year background in molecular exercise physiology and Dan started his medical training in cardiology and now uses high-dimensional data to understand heart failure. MoTrPAC (Molecular Transducers of Physical Activity Consortium) is a NIH-funded initiative studying exercise effects across multiple sites in the US using both human and animal studies. Dan and Malene explain what the dataset is all about, including what data is collected, from where, and the kinds of raw and pre-processed data available. They also describe how it can be accessed through various user-friendly tools for scientists of all technical abilities. Find out more at https://motrpac-data.org/ and contact them with questions at motrpac-helpdesk@lists.stanford.edu.

Guest Bios

Daniel Katz, MD, is an Assistant Professor of Medicine in the Stanford Center for Biomedical Informatics Research (BMIR) and the Cardiovascular Medicine Divisions. He practices as an Advanced Heart Failure and Transplant Cardiologist. He completed internal medicine residency at Massachusetts General Hospital, general cardiology training at Beth Israel Deaconess Medical Center, and joined Stanford in 2021 for his advanced heart failure training. His research focuses on identifying the various pathophysiological patterns and mechanisms that lead to the heterogeneous syndrome of heart failure. His efforts leverage high dimensional data in many forms including clinical phenotypes, plasma proteomics, metabolomics, and genetics. He is presently engaged in analysis of multi-omics data from MoTrPAC, and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.

Malene Lindholm, PhD, is a senior research engineer and Director of the Human Molecular Athlete Moonshot for the Wu Tsai Human Performance Alliance at Stanford University. After earning her PhD in Medicine at the Karolinska Institute in Sweden, she performed her postdoctoral studies at the Cardiovascular Institute at Stanford University. Her current research focuses on unraveling the multi-omics adaptation mechanisms to exercise across tissues and the genetic basis of extreme human performance. The ultimate objective is to transform the findings into tangible application in the field of precision exercise health and medicine.

Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

Episode 08: An Early Career Researcher’s Experience with the Common Fund Data Ecosystem

Description

In this episode, Allissa Dillman interviews Seth Berke about his educational journey from pre-med to genomics, and the importance of public datasets and cloud computing in scientific research. Seth highlights the value of, and opportunities provided by the Common Fund Data Ecosystem for early career researchers, shares insights on integrating multiple types of biomedical data, and offers advice for students interested in the field of genomics. Seth’s recent publication referenced in the podcast can be found here: Fundamentals of FAIR biomedical data analyses in the cloud using custom pipelines.

Guest Bio

Seth Berke is a graduate of Johns Hopkins University with a B.S. in Molecular and Cellular Biology and an incoming Ph.D. student in Quantitative and Computational Biology at Princeton. His undergraduate research focused on leveraging biomedical cloud platforms to analyze large-scale pediatric genomics data, work that led to presentations at several international conferences and recent manuscript acceptances in leading journals. He currently works as a Research Assistant at the Johns Hopkins Bloomberg School of Public Health, contributing to the NIH Acute to Chronic Pain Signatures Consortium by integrating and analyzing multimodal data. Seth aims to advance precision health by uniting data across biopsychosocial domains to improve the prediction and treatment of complex disease phenotypes.

Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

Episode 07: Insights into Training and Engagement with CFDE Data

Description

In this episode, Allissa Dillman chats with Kelli Bursey about the current state of training and mentoring within the Common Fund Data Ecosystem (CFDE), and Diane Krause about academic engagement with CFDE datasets. This information was gathered by the Training Center as part of a mixed-methods landscape analysis used to identify necessary CFDE data science skills for CFDE learners, describe the existing CFDE training landscape, and identify needed training activities and resources for CFDE learners and stakeholders.

Guest Bios

Kelli Bursey has over 15 years of experience at ORAU as a mixed-method researcher, evaluator, and project manager. She actively contributes to a diverse range of evaluation, health communication, and formative research projects for government agencies and private sector clients, providing strategic insights and expertise to drive project success. Ms. Bursey has served over 12 years on the Institutional Review Board (IRB) and currently serves as the Chair for Oak Ridge Sitewide Institutional Review Board, and is well-versed in federal regulations for the protection of human subjects. She holds a certificate of proficiency in qualitative research and brings strong skills in both qualitative and quantitative analysis.

Diane Krause, MS-MPH, CPH, RDN, has over 15 years of experience in health communication at ORAU, specializing in media monitoring, social media listening, and information analysis. She contributed to a landscape analysis for the CFDE Training Center, where she analyzed scholarly output related to CFDE datasets and online discussions of related technical topics.

Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).