Faculty Spotlight: Dr. Guanglan Zhang


Dr. Guanglan Zhang

Meet Dr. Guanglan Zhang, Assistant Professor at MET Computer Science and the faculty coordinator of the Health Informatics program. Dr. Zhang earned a PhD and MS from Nanyang Technological University, MS from Northwestern Polytechnic University and a BS from Luoyang Institute of Technology. She has been teaching online and on campus courses at BU since 2011. This summer, she is the instructor for MET CS 544 S A1 Foundations of Analytics with R on the Charles River campus.

What is your area of expertise?
I have a broad background in bioinformatics and health informatics. My expertise includes computational modeling of complex biological processes, such as the identification of vaccine targets, as well as the analysis of biomedical data, including mass spectrometry (MS), microarray, and sequencing data. My experience also includes the building of analytical tools for pattern recognition from these data, the development of next-generation biological databases, the design of diagnostic tools, the implementation of medical algorithms to support clinical decision-making, the knowledge discovery from mining of socioeconomic data, and the building of computational infrastructures to support health care needs.

When working in Dana-Farber Cancer Institute (DFCI), Boston, to facilitate efficient large-scale data collection, integration, storage, and analysis, I developed a framework, named KB-builder, for the rapid development of next-generation biological databases. KB-builder is modularized and can be rapidly deployed to any project that involves biological data storage, retrieval, annotation, and analysis. It has been deployed in multiple projects (HLA genotyping, Epstein-Barr virus, Merkel cell polyomavirus, influenza A virus, flaviviruses, human papilloma virus, and tumor T-cell antigens). Most of the online bioinformatics systems I developed while working in DFCI have been used not only by our colleagues at DFCI but also by the researchers and labs who work on immunology and cancer vaccines.

What has been your focus since coming to Boston University?
Since moving to Boston University, I have extended my research interests to the area of health informatics and have been leading research activities in MET’s Health Informatics Research Lab. Together with Jon Long, a master’s student with pharmacy background, we developed the Antidote Application (AA), a computational system that automatically provides patient-specific antidote treatment recommendations and individualized dose calculations. To the best of our knowledge, AA is the first educational decision support system in toxicology that provides patient-specific treatment recommendations and drug dose calculations, and it can potentially be used as a stand-alone clinical decision tool or as a component in an electronic health record system. I have presented our work in multiple conferences and invited talks.

Electronic Data Capture (EDC) is used to facilitate rapid, accurate, and error-free collection of data for clinical studies and medical surveillance. A full-featured Mobile EDC (mEDC) solution with an asynchronous data transport layer will better meet the needs of distributed studies in resource-constrained geographical areas. Together with Caleb Ruth, a master’s student with years of software development experience, we developed ConnEDCt, a full-featured mEDC application that is customizable for a variety of longitudinal study protocols, with regulatory-compliant security, auditability and an asynchronous data transport model that allows ad hoc synchronization with a cloud-based server. Currently a group of researchers in Cornell University is using ConnEDCt as the data capturing tool for a clinical trial in India.

At ChengDu

Dr. Zhang presenting at “Huaxi International Medical Valley” construction seminar in Chengdu.

Please tell us about your work. Can you share any current research or recent publications?
Last year, I started to collaborate with Dr. Bindu Kalesan, director of Evans Center for Translational Epidemiology and Comparative Effectiveness at BU’s School of Medicine, on a longitudinal study of effects of violence and traumatic events (EVE) using a cohort approach. We formed a software development team comprised of four MET master’s students and developed a prototype website to support the study (everesearch.org).

During my twenty years of pursuing research in biomedical sciences, I have authored more than fifty journal publications, developed more than twenty online computational systems, and filed two patents as co-inventor. I have a demonstrated record in the development of online computational systems and databases that support clinical research, biomedical data mining, and knowledge discovery. The computational tools that I developed have been used in the study of cancer, immunology, vaccinology, toxicology, and infectious disease. Through the development of advanced computational solutions, I have contributed to the rapid progress of biomedicine and health informatics.

This past March, I was invited to give a few talks in China. At Peking University Peoples Hospital, in Beijing, I presented “TANTIGEN: a comprehensive database of tumor T cell antigens.” Then, at the 2018 “Huaxi International Medical Valley” construction seminar in Chengdu entitled “Clinical Decision Support Systems and Evidence-based Medicine—Antidote Application: A System for Treatment of Common Toxin Overdose.” 

I have several recent publications that you can see listed on my faculty profile page.

What courses do you teach?
I am the faculty coordinator for the Health Informatics program, in which I teach Biomedical Sciences and Health IT (MET CS 570) and Health Informatics (MET CS 580). I also teach Foundations of Analytics with R (MET CS 544), Data Analysis and Visualization with R (MET CS 555), and Web Analytics and Mining (MET CS 688).

Please highlight a particular course project that most interests your students.
The term project in CS 580 requires students to understand the logic of selected medical algorithms. We don’t require students to implement the algorithm considering some students don’t have a programming background. But, a group of students in previous semesters did combine the CS 580 term project with their term project in Software Engineering (MET CS 673), and implemented an online system mimicking the clinical decision process for treatment of type II diabetes. Students presented this work at the 2015 Computer Science and Education in Computer Science (CSECS) conference.