"Translational Biomedical and Health Informatics for Personalized Healthcare
Biomarker Pipeline and Molecular Tissue Imaging Informatics Tool TissueWiki"
Rapid advancement in biotechnologies such as –omic (genomics, proteomics, metabolomics, lipidomics etc.), bionanotechnologies, molecular imaging, mobile, and wearable sensors etc. gives hope that 5P medicine (personalized, predictive, pervasive, preventive, and participatory) will become a reality. To have personalized clinical and day-to-day health monitoring, a key element is “Biomedical and Health Informatics (BMHI)”. In this talk, I will present an integrated BMHI framework that analyzes multi-scale (i.e. molecular, cellular, organ, and whole body) and multi-modal data. I will illustrate our theme of Bio-Nano-Info Integration for Personalized Health by a translational pipeline, which enables and speeds up discovery, development, and delivery: (A) to discover the genetic and protein bio-molecular signatures (biomarkers) that diagnose and treat disease based on the molecular profiles of each individual; (B) to conduct cellular and molecular imaging analysis and informatics on huge amounts of data generated from bio-conjugating nanoparticles (e.g. semiconductor quantum dots and iron oxide nanocrystals) probes with biomarkers for disease imaging and targeted therapeutics; and then (C) to integrate the resulting data for interpretation and to deliver to users through mobile devices.
I will expand on TissueWiki (http://tissuewiki.bme.gatech.edu), a comprehensive tissue and molecular imaging informatics system that archives multi-terabytes of raw and processed meta-information from Immunohistochemistry (IHC) data captured in Human Protein Atlas (HPA) database; tissue imaging mass spectrometry (TIMS) data acquired locally at Georgia Tech’s Center of Imaging Mass Spectrometry; and high resolution multiplexed Quantum Dots (QD) imaging data acquired by Virtual Miscopy Technology at Emory-Georgia Tech Cancer Nanotechnology Center. TissueWiki provides image and antibody meta-information such as quality scoring, segmentation masks, histograms, cell statistics, and color space conversions etc. The meta information enhances data sharing, evaluation, validation, and ranking of antibody candidates. We have used Wiki framework to enhance the accessibility of data, and to enable searches at the semantic level. Other bioinformatics tools such as (1) caCORRECT that improves genomics data quality, (2) omniBiomarker that identifies biomarkers from high throughput –omic data based on clinical knowledge, (3) Q-IHC/omniSpect that quantifies multiplex in vitro diagnostic imaging data, have passed multi-step rigorous review by NIH/NCI cancer Biomedical Informatics Grid (caBIG), and have received caBIG silver-level compatibility certification. They are currently available at (https://cabig.nci.nih.gov/tools/caCorrect/), “omniBiomarker” (https://cabig.nci.nih.gov/tools/Omnibiomarker). In addition, I will also show our work with regulatory agency such as FDA through Microarray Quality Control Consortium (MAQC).
Dr. Wang’s primary research interest is translational biomedical and health informatics for systems medicine and health monitoring. Her research team performs clinical biomarker quality control and analysis; molecular and cellular imaging quantification and informatics; modeling and visualization; and delivery through mHealth. As corresponding author, Prof. Wang has published in journals such as Annals of Biomedical Eng, BMC Bioinformatics, Trends in Biotechnology, Nature Protocols, Proceedings of National Academy of Sciences, and Annual Review of Medicine. With Microsoft Research and Hewlett Packard Corp as industrial partners, Dr. Wang has played an essential role in Emory-Georgia Tech Cancer Nanotechnology Center, and is the Director for Bioinformatics and Biocomputing Core. Her team has developed multiple bioinformatics software systems, and caCORRECT and omniBiomarker etc. have been certified by National Cancer Institute (NCI/NIH) cancer Biomedical Informatics Grid (caBIG) as silver-level compatible (https://cabig.nci.nih.gov/tools/caCorrect/ and https://cabig.nci.nih.gov/tools/Omnibiomarker). In addition, she has been active in FDA-led Microarray Data Analysis and Next Generation Sequencing consortium (MAQC).
Dr. Wang received Distinguished Cancer Scholar Award from Georgia Cancer Coalition in 2004, a university level Outstanding Undergraduate Research Faculty Mentor Award from Georgia Institute of Technology in 2005, and an Outstanding Service Award from IEEE BIBE in 2007. Dr. Wang received Ph.D.EE, multidisciplinary MS degrees (EE, Applied Math, and CS) from Georgia Tech in USA, and BEng from Tsinghua University in China. In addition, Dr. Wang has several years of industrial R&D experience in the former AT&T Bell Labs, Intel Architecture Labs, Hughes Research Labs, Lucent Technologies Bell Labs, and Agere Systems. Dr. Wang is currently serving as the Technical Committee Co-Chair of Information Technology for Health in IEEE Engineering in Medicine and Biology Society (EMBS).