Biomedical Big Data Analytics for Outcome-Driven Precision Health
Rapid advancements in biotechnologies such as –omic (genomics, proteomics, metabolomics, lipidomics etc.), next generation sequencing, bio-nanotechnologies, molecular imaging, and mobile sensors etc. accelerate the data explosion in biomedicine and health wellness. Nations around the world have been seeking novel effective ways to make sense of “Big Data” for evidence-based, outcome-driven, and affordable 5P (Patient-centric, Predictive, Preventive, Personalized, and Precise) health care. The goal is to use multi-modal and multi-scale (i.e. molecular, cellular, whole body, individual, and population) biomedical data analytics to enable and accelerate discovery, development, and delivery.
First, I will highlight major challenges in biomedical health informatics pipeline consisting of data quality control, information feature extraction, advanced knowledge modeling, decision making, and action through feedback. Second, I will present utilities of health analytics for translational medicine such as histopathological imaging informatics for improving clinical decision support; RNA-seq data analytics to achieve improved biological utility, reproducibility, and effectiveness in decision making; and Electronic Health Record data quality control and mining. Third, I will discuss emerging research directions such as integrating genomics with EHR. Last, there is a shortage of data scientists and engineers who are capable of handling Big Data to meet the need of healthcare stakeholders (i.e. patients, physicians, payers, and hospitals). I will discuss efforts such as patient-centric educational intervention, community-based crowd sourcing, and Biomedical Data Analytics MOOC development.
Our research has been supported by NIH, NSF, Georgia Research Alliance (GRA), Georgia Cancer Coalition (GCC), Emory-Georgia Tech Cancer Nanotechnology Center, Children’s Health Care of Atlanta (CHOA), Atlanta Clinical and Translational Science Institute (ACTSI), USA Centers for Disease Control and Prevention (CDC), and industrial partners such as Microsoft Research and HP.
May D. Wang, Ph.D. is a full professor in the Joint Department of Biomedical Engineering, School of Electrical and Computer Engineering, Winship Cancer Institute, Hematology and Oncology, IBB, and IPaT of Georgia Institute of Technology and Emory University. She is a Kavli Fellow, a Georgia Research Alliance Distinguished Cancer Scholar, and a Fellow of the American Institute for Biological and Medical Engineering (AIMBE). Dr. Wang serves as Co-Director of Biomedical Informatics Program of Georgia Tech in Atlanta Clinical and Translational Science Institute (ACTSI), Co-Director of Georgia-Tech Center of Bio-Imaging Mass Spectrometry, and Biocomputing and Bioinformatics Core Director in Emory-Georgia-Tech Cancer Nanotechnology Center.
Dr. Wang’s research interest is in Biomedical Big Data Analytics, with a focus in Biomedical and Health Informatics (BHI) for personalized and precision health. She works in high throughput next-generation-sequencing and -omic data mining to identify clinical biomarkers, bionanoinformatics, pathological imaging informatics to assist clinical diagnosis, critical and chronic care health informatics for evidence-based health decision support, and predictive systems modeling to improve health outcome. She is the corresponding/co-corresponding author for articles published in Journal of American Medical Informatics Association (JAMIA), Journal of Biomedical and Health Informatics (JBHI), IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Briefings in Bioinformatics, BMC Bioinformatics, Journal of Pathology Informatics, Proceedings of The IEEE, IEEE Transactions on Biomedical Engineering (TBME), Proceedings of National Academy of Sciences (PNAS), Annual Review of Medicine, Nature Protocols, Circulation Genetics, Nanomedicine, BMC Medical Imaging, Annals of BME (ABME), and Trends in Biotechnology etc. She has led RNA-data analysis investigation within FDA-led Sequencing Consortium (SEQC) of MAQC-III.
Prof. Wang is the Senior Editor for IEEE J-BHI, an Associate Editor for IEEE TBME, and IEEE Big Data Initiative Steering Committee member. She has served as an Emerging Area Editor for Proceedings of National Academy of Science (PNAS), IEEE EMBS Biomedical and Health Informatics (BHI) Technical Committee Chair, IEEE EMBS BHI Conference Steering Committee Chair, International Conference on Biomedical and Health Informatics Conference Co-Chair or Technical Program Co-Chair, ACM Bioinformatics, Computational Biology and Health Informatics Conference Co-Chair or Steering Committee Co-Chair, and 2016 EMBS Annual Conference Co-Chair. Dr. Wang was nominated to be 2014-2015 IEEE EMBS Distinguished Lecturer, and was elected to be 2015-2017 AdCom member and 2017-2018 VP Finance. In addition, Dr. Wang has devoted to the training of young generation of data scientists and engineers, and is a recipient of Georgia-Tech’s Outstanding Faculty Mentor for Undergraduate Research and a recipient of Emory University’s Millipub Club Award (research publication received over 1,000 citations).