Mei Liu, PhD, a computer scientist who uses advanced informatics approaches to improve health care, will join this fall the NJIT College of Computing Sciences as an assistant professor. Her talents will combine with those of more than 20 new faculty members to add momentum to NJIT’s strategic plan for making a major impact on the quality of life in the 21st century. This interdisciplinary initiative is focused on three vital areas: convergent life science and engineering, “digital everyware” — ubiquitous computing — and sustainable systems.The women and men joining NJIT to serve a growing student body bring expertise that spans diverse supporting clusters. These include advanced manufacturing, architecture design and construction, big data, biochemistry, business systems, material science and engineering, and sensing and control.
“NJIT’s academic status and interdisciplinary strategy have attracted people at various stages of their careers, and who offer NJIT both distinctive abilities and new resources,” says Provost Ian Gatley. “Enthusiasm for NJIT’s interdisciplinary commitment was apparent during the search process. Everyone interviewed spoke about how the problems they work on are inherently interdisciplinary, how they like to work on teams, how they look forward to collaborating with colleagues across disciplines.”
Donald Sebastian, NJIT’s senior vice president for research and development, emphasizes that connecting with real-world issues is at the heart of expectations for a technological research university. “Academic disciplines are the core of the university and the framework for learning. However, their alignment with industries of the future is not as obvious as with those sectors that have prevailed over the last century. Our strategic research thrusts are designed to make those 21st-century connections explicit.” Convergent life science and engineering, digital everyware and sustainable systems — themes that transcend departments or colleges — shaped NJIT’s hiring plan, he adds.
Liu’s long-term research goal is to develop data-mining methodologies to uncover clinical knowledge from Electronic Medical Records (EMRs) that improves the quality, safety, efficiency and effectiveness of health care. EMRs have created an unprecedented resource for observational studies as it contains not only detailed patient information but also large amounts of longitudinal clinical data. Despite the promise of EMR as a research tool, challenges exist for large-scale observational studies. First, much of the relevant clinical information is embedded in narrative text. Second, multiple factors conspire to make drawing specific conclusions from EMR data more challenging than data collected specifically to answer research hypotheses. Thus, it is highly desirable to develop effective and efficient computational methods to mine EMR data for conducting large-scale observational research.
Adverse drug reaction (ADR) for instance, is one of the major causes for failure in drug development. And severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity, as exemplified by numerous drug withdrawals. Currently, she is leading three projects to simultaneously examine ADRs from different angles. First, she aims to predict ADRs from the chemical, biological, and phenotypic properties of drugs. Second, she uses laboratory and retrospective medication order data from EMR to ascertain ADRs. Third, she is exploring the use of natural language processing (NLP) techniques to extract adverse events from the narrative notes in EMR and correlate those events with medications through association mining. She is also interested in other data mining tasks for clinical informatics, such as drug repurposing (i.e. application of known drugs to new diseases). She is also interested in using patient medical records to build predictive models for diseases such as diabetes and cancer.
In addition, she plans to combine her experience in bioinformatics and clinical informatics to better understand how molecules function together in achieving a particular clinical outcome. For instance, she plans to utilize her knowledge in protein/gene interaction networks to study ADRs because a drug acts on a human body by inducing perturbations to biological systems, which involve various molecular interactions such as protein-protein interactions, signaling pathways, and pathways of drug action and metabolism. She’s especially interested in understanding gene-disease relationships; this understanding will assist in the designs of new drug and therapeutic treatments.
Liu has published numerous papers in top journals including Bioinformatics, PLoS One, and JAMIA (Journal of American Medical Informatics Association) and conferences such as AMIA (American Medical Informatics Association Annual Symposium).
She received her PhD in computer science with a research focus in bioinformatics from the University of Kansas in 2009. Her dissertation involved the development and optimization of data mining algorithms for understanding protein interactions and protein functions. She developed a domain-based random decision forest framework (RDFF) to predict protein interactions and a cross-species interacting domain patterns (CSIDOP) approach to discover new functions of proteins. She also designed a method called K-GIDDI that expanded existing knowledge in protein domain-domain interaction (DDI). Upon receiving her doctorate, she was awarded the National Library of Medicine Postdoctoral Training Fellowship in biomedical informatics, and completed the training in the department of biomedical informatics at Vanderbilt University in 2012.