Guanglan Zhang
Associate Professor Chair, Computer Science
Dr. Zhang’s research focus has been in machine learning, data mining, and knowledge management in the biomedical and healthcare fields. Her major research interests include computational modeling of complex biological processes, such as the identification of vaccine targets, the development of a framework for rapid development of next-generation biological databases, the building of analytical tools for pattern recognition from biomedical data, and the design of diagnostic tools. She has authored more than thirty journal publications, developed more than twenty online computational systems, and filed two patents as co-inventor. Through the development of advanced computational solutions, she contributes to the rapid progress of basic and applied biology and biomedicine. Zhang served as a research associate at Harvard Medical School, senior bioinformatics engineer at Dana-Farber Cancer Institute, and project leader and senior research engineer at the Institute for Infocomm Research (previous name: Kent Ridge Digital Lab), Singapore.
Courses
-
- MET CS 580 – Health Informatics
Scholarly Works
Journal Articles
Huiting, L. N., Samaha, Y., Zhang, G. L., Roderick, J. E., Li, B., Anderson, N. M., Wang, Y. W., Wang, L., Laroche, F. J. F., Choi, J. W., Liu, C. T., Kelliher, M. A., Feng, H. “UFD1 contributes to MYC-mediated leukemia aggressiveness through suppression of the proapoptotic unfolded protein response.” Leukemia (in press).
Yoshizawa, A., Bi, K., Keskin, D. B., Zhang, G., Reinhold, B., and Reinherz, E. L. “TCR‐pMHC encounter differentially regulates transcriptomes of tissue‐resident CD8 T cells.” European Journal of Immunology 48, no. 1 (2018): 128–150.
Olsen, L. R., Tongchusak, S., Lin, H., Reinherz, E. L., Brusic, V., Zhang, G. L. “TANTIGEN: a comprehensive database of tumor T cell antigens.” Cancer Immunology, Immunotherapy 66, no. 6 (2017): 731–735.
Bae, J., Hideshima, T., Zhang, G. L., Zhou, J., Keskin, D., Munshi, N., Anderson, K. “Identification and characterization of HLA-A24 specific XBP1, CD138 (Syndecan-1), and CS1 (SLAMF7) peptides inducing antigens-specific memory cytotoxic T lymphocytes targeting multiple myeloma.” Leukemia 32 (2017): 752–764.
Hoft, D. F., Xia, M., Zhang, G. L., Blazevic, A., Tennant, J., Kaplan, C., Matuschak, G., Dube, T. J., Hill, H., Schlesinger, L. S., Andersen, P. L., Brusic, V. “PO and ID BCG vaccination in humans induce distinct mucosal and systemic immune responses and CD4+ T cell transcriptomal molecular signatures.” Mucosal Immunology (2017).
Olsen, L.R., Kudahl, U.J., Simon, C., Sun, J., Schönbach, C., Reinherz, E.L., Zhang, G.L., and Brusic, V. “BlockLogo: Visualization of peptide and sequence motif conservation.” Journal of immunological methods 400-401 (2013): 37-44.
Yamada, T., Muta, E., Kim, J., Azuma, K., Sugawara, S., Zhang, G.L., Matsueda, S., Yamashita, Y., Itoh, K., Hoshino, T., and Sasada, T. “EGFR T790M Mutation as a Possible Target for Immunotherapy; Identification of HLA-A*0201-Restricted T Cell Epitopes Derived from the EGFR T790M Mutation.” PLOS One 8, no. 11 (2013): e78389.
Riemer, A.B., Keskin, D.B., Zhang, G.L., Handley, M.I, Anderson, K.S., Brusic, V., Reinhold, B., and Reinherz, E.L. “A conserved E7-derived CTL epitope expressed on human papillomavirus-16 transformed HLA-A2+ human epithelial cancers.” Journal of Biological Chemistry 285, no. 38 (September 2010).
Lin, H.H., Zhang, G.L., Tongchusak, S., Reinherz, E.L., and Brusic, V. “Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.” BMC Bioinformatics 9, suppl 12 (December 2008): s22.
Khan, A.M., Miotto, O., Nascimento, E.J., Srinivasan, K.N., Heiny, A.T., Zhang, G.L., Marques, E.T., Tan, T.W., Brusic, V., Salmon, J., and August, J.T. “Conservation and variability of dengue virus proteins: implications for vaccine design.” PLoS Neglected Tropical Diseases 2, no. 8 (2008): e272.
Zhang, G.L., Khan, A.M., Srinivasan, K.N., Heiny, A.T., Lee, K.X., Kwoh, C.K., August, J.T., and Brusic, V. “Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes.” BMC Bioinformatics 9, suppl 1 (February 2008): S19.
Heiny, A.T., Miotto, O., Srinivasan, K.N., Khan, A.M., Zhang, G.L., Brusic, V., Tan, T.W., and August, J.T. “Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets.” PLOS ONE 2, no. 11 (November 2007): e1190.
Zhang, G.L., Bozic, I., Kwoh, C.K., August, J.T., and Brusic, V. “Prediction of Supertype-Specific HLA Class I Binding Peptides Using Support Vector Machines.” Journal of Immunological Methods 320, no. 1-2 (March 2007): 143-154.
Chong, A., Zhang, Z., Choi, K.P., Choudhary, V., Djamgoz, M.B., Zhang, G.L., and Bajic,V.B.”Promoter profiling and coexpression data analysis identifies 24 novel genes that are coregulated with AMPA receptor genes, GRIAs.” Genomics. 89, no. 3 (2007): 378-84.
Rajapakse, M., Zhang, G.L., Srinivasan, K.N., Schmidt, B., Petrovsky, N., and Brusic, V. “PREDNOD, a prediction model for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse.” Autoimmunity 39, no. 8 (2006): 645-50.
Zhang, Z.H., Koh, J.L.Y., Zhang, G.L., Choo, K.H., Tammi, M.T., and Tong, J.C. “AllerTool: A Web Server for Predicting Allergenicity and Allergic Cross-Reactivity in Proteins. Bioinformatics 23, no. 4 (2006): 504-6.
Zhang, G.L., Petrovsky, N., Kwoh, C.K., August, J.T., and Brusic, V. “PREDTAP: a system for prediction of peptide binding to the human transporter associated with antigen processing. Immunome Research 2, no. 1 (2006): 3.
Cocquet, J., Chong, A., Zhang, G.L., and Veitia, R.A. “Reverse transcriptase template switching and false alternative transcripts.” Genomics 88, no. 1 (2006): 127-31.
Tong, J.C., Zhang, G.L., Tan, T.W., August, J.T., Brusic, V., and Ranganathan, S.
“Prediction of HLA-DQ3.2β Ligands: evidence of multiple registers in class II binding peptides.” Bioinformatics 22 (2006): 1232-1238.
Zhang, G.L., Khan, A.M., Srinivasan, K.N., August, J.T., and Brusic, V. “Neural Models for Predicting Viral Vaccine Targets.” Journal of Bioinformatics and Computational Biology 3, no. 5 (2005): 1207-1225.
Zhang, G.L., Khan, A.M., Srinivasan, K.N., August, J.T., and Brusic, V. “MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.” Nucleic Acids Research 33 (2005): W172-W179.
Zhang, G.L., Srinivasan, K.N., Veeramani, A., August, J.T., and Brusic, V. “PREDBALB/c: a system for prediction of peptide binding to the H2d molecules, a haplotype of the BALB/c mouse.” Nucleic Acids Research 33 (2005): W180-W183.
Srinivasan, K.N., Zhang, G.L., Khan, A.M., August, J.T. and Brusic, V. “Predictions of Class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens.” Bioinformatics 20, suppl 1, (2004): i297-i302.
Chong, A.*, Zhang, G.L.* and Bajic, V.B. “ICE (Information for the Coordinates of Exons): A Human Splice Sites Database.” Genomics 84 (2004): 762-766. (*These authors contributed equally to the work)
Chong, A., Zhang, G.L., and Bajic, V.B. “FIE2: A program for the extraction of genomic DNA sequences around the start and translation initiation site of human genes.” Nucleic Acids Research 31, no. 13 (2003): 3546-3553.
Chong, A., Zhang, G.L., and Bajic, V.B. “Information and Sequence Extraction around 5′-end and Translation Initiation Site of Human Genes.” In Silico Biology 2 (2002): 462-465.
Brusic, V., Petrovsky, N., Zhang, G.L., and Bajic, V.B. “Prediction of promiscuous peptides that bind HLA class I molecules.” Immunology and Cell Biology 80, no. 3 (2002): 280-285.
Bajic, V.B., Seah, S.H., Chong, A., Zhang, G.L., Koh, J.L.Y., and Brusic, V. “Dragon Promoter Finder: recognition of vertebrate RNA Polymerase II promoters.” Bioinformatics 18, no. 1 (2002): 198-199.
Abeyratne, U.R., Zhang, G.L., and Saratchandran, P. “EEG Source Localization: A Comparative Study of Classical and Neural Network Methods.” International Journal of Neural Systems 11, no. 4 (2001): 349-359.
Tun, A.K., Lye, N.T., Zhang, G.L., Abeyratne, U.R., and Saratchandran, P. “RBF networks for source localization in quantitative electrophysiology.” Critical Reviews in Biomedical Engineering 28 (2000): 463-472.
Abstracts
Finstad, S.L., Zhang, G.L., Linde, C., Muik, A., Hermann, F., Evans, V., de la Rosa, M, Zahn, R, Gaufin, T., Reimann, K., Apetrei, C., Miller, C., McCune, J., Picker, L., Veazey, R., Brusic, V., Letvin, N., and Schmitz,J. “Influence of FC Gamma-receptor polymorphisms on efficacy of antibody-mediated lymphocyte depletion in rhesus macaques.” Journal of Medical Primatology 39, no. 4 (2010): 279 -280.
Biernacki, M., Alonso, A., Zhang, G.L., Zhang, L., Zhang, W.D., Tai, Y.T., Munshi, N., Alyea, E.P., Soiffer, R.J., Brusic, V., Ritz, J., Anderson, K.C., and Wu, C.J. “DAPK2 and PIM1 Are Myeloma-Associated Antigens That Elicit Coordinated B and T Cell Immunity After Syngeneic HSCT.” Blood (ASH Annual Meeting Abstracts) 114, no. 22 (2009): 2445.
Biernacki, M., Zhang, G.L., Zhang, W.D., Brusic, V., Soiffer, R.J., Neuberg, D., Alyea, E.P., Tai, Y.T., Munshi, N.C., Ritz, J., Anderson, K.C., and Wu, C.J. “Novel Myeloma-Associated Antigens Revealed in the Context of Successful Syngeneic Hematopoietic Stem Cell Transplantation.” Blood (ASH Annual Meeting Abstracts) 112, no. 11 (2008): 815.
Book Chapters
Handoko, S.D., Kwoh, C.K., Ong, Y.S., Zhang, G.L., and Brusic, V. “Extreme Learning Machine for Predicting HLA-Peptide Binding.” Lecture Notes in Computer Science 3973, Springer (2006): 716-721.
Bozic, I., Zhang, G.L., and Brusic, V. “Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers.” Lecture Notes in Computer Science 3578, Springer (2005): 375-381.
Zhang, G.L. Abeyratne, U.R., and Lee, T.H. “A Systematic Comparison of Classical and Neural Network Techniques in EEG Source Localization.” Computer Methods in Biomechanics and Biomedical Engineering-3, edited by Middleton, J., Jones, M.L., and Pande, G.N. Gordon & Breach Science Publishers, Amsterdam, Netherlands (2000).
Conference Articles
Long, J., Zhang, Y., Brusic, V., Chitkushev, L., Zhang, G.L. “Antidote Application: an educational system for treatment of common toxin overdose.” ACM-BCB, Boston, Mass., August 20–23, 2017. Presented by Jon Long.
Zhang, P., Chitkushev, L., Brusic, V., and Zhang, G.L. “Biomarkers in immunology: from concepts to application.” In Proc. International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB). Washington, D.C., September 22-25, 2013.
Sun, J., Zhang, G.L., Olsen, L.R., Reinherz, E.L., and Brusic, V. “Landscape of neutralizing assessment of monoclonal antibodies against dengue virus.” In Proc. International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB). Washington, D.C., September 22-25, 2013.
Zhang, G.L., Riemer, A., Keskin, D.B., Chitkushev, L., Reinherz, E.L., and Brusic,V. “HPVdb: Data Source and Analysis Platform for T-cell based Vaccine Target Discovery in Human Papillomavirus.” In Proc. International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB). Washington, D.C., September 22-25, 2013.
Chitkushev, L., Dilek, E., Lee J. W., Zhang, G. L., and Zlateva, T. “Data Cleaning of Probe Signals from CDNA Tiling Microarray: Outlier Detection, Noise Reduction, and Identification of Uninformative Probes in HLA Typing Application.” Proc. of Bioinformatics and Biomedicine Workshops on Informatics Applications in Therapeutics (Atlanta, Ga., 2011): 682.
Brusic, V., Chitkushev, L., Kalathur, S., Zhang, G. L., and Zlateva, T. “Visualization Tools for Presenting and Analysis of Global Landscapes of Vaccine Targets.” Proc. Bioinformatics and Biomedicine Workshops on Informatics Applications in Therapeutics (Atlanta, Ga., 2011): 683–88.
Chitkushev, L., Zlateva, T., DeLuca, D., Zhang, G.L., and Brusic, V. “Integrated health informatics curricula within information technology programs.” In Proceedings of 6th Annual International Conference on Computer Science and Education in Computer Science. Munich, Germany (2010): 204‐216.
Zhang, G.L., Tong, J.C., Zhang, Z.H., Zheng, Y., August ,J.T., Kwoh, C.K., and Brusic, V. “Computational models for identifying promiscuous HLA-B7 binders based on information theory and support vector machine.” International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 2006.
Zhang, G.L., Kwoh, C.K., August, J.T., and Brusic, V. “Performance Evaluation of MULTIPRED1 on Prediction of MHC Class I Binders.” International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 2006.
Bajic, V.B., Koh, J.L.Y., Chong, A., Seah, S.H., Zhang, G.L., and Brusic, V. “Dragon Promoter Finder ver. 1.2e: A system for promoter finding and analysis.” BioMedical 2001, Singapore, Book of Abstracts (2001): 19-21.
Zhang, G.L., Abeyratne, U.R., and Saratchandra, P. “Comparing RBF & BPN Neural Networks in Dipole Localization.” In Proceedings of The First Joint BMES/EMES Conference Serving Humanity, Advancing Technology. Atlanta, Ga. (October 13-16, 1999): 939.
Adnan, S.M., Abeyratne, U.R., and Zhang, G.L. “Position selective stimulation of a multifascicular nerve.” In Proceedings of The First Joint BMES/EMES Conference Serving Humanity, Advancing Technology. Atlanta, Ga. (October 13-16, 1999): 480.
Posters
Zhang, G.L., Khan, A.M., and Brusic, V. “Fighting Against Dengue: Computer-Aided Vaccine Design.” Inaugural SERC Inter-RI PosterSymposium, September 19, 2005.
Zhang, G.L., Bozic, I., Kwoh, C.K., and Brusic, V. “MULTIPRED: a computational system for prediction of promiscuous HLA class I binders.” 2nd International Immunoinformatics Symposium and Immunoinformatics workshop (IIMMS2005). Boston, Mass., March 7-9, 2005.
Khan, A.M., Zhang, G.L., Srinivasan, K.N., August, J.T., Tan, T.W., and Brusic, V. “Analysis of antigenic hot-spots in dengue virus: A bioinformatics approach.” 1st Asian Regional Dengue Research Network Meeting. Bangkok, Thailand, October 18-20, 2004.
Zhang, G.L., Kwoh, C.K., and Brusic, V. “MULTIPRED: a computational system for prediction of promiscuous HLA class I binders.” 8th NUS-NUH Annual Scientific Meeting. NUS, Singapore, October 7, 2004.
Chong, A., Zhang, G.L., and Bajic, V.B. “FIE: Sequence information extraction around the 5′-end of human genes.” Bioinformatics and Genome Research Conference, San Diego, Calif., June 2-7, 2002.
Bajic, V.B., Koh, J.L.Y., Chong, A., Seah, S.H., Zhang, G.L., and Brusic, V. “Dragon Promoter Finder ver. 1.2e: A system for promoter finding and analysis.” BioMedical 2001, Singapore.
Bioinformatics Systems Developed
SprPred: a system for large-scale screening of allele-, genotype-, and supertype-specific HLA associated peptides.
Tumor T-cell Antigen Database: A data source and analysis platform for cancer vaccine target discovery focusing on human tumor antigens that contain HLA ligands and T cell epitopes.
Flavivirus Antigen Database: A data source and analysis platform for flavivirus immune target discovery by focusing on antigens that contain cross protective T cell and B cell epitopes.
Influenza A Virus Antigen Database: A data source and analysis platform for Influenza A virus immune target discovery by focusing on antigens that contain cross protective T cell and B cell epitopes.
FcgR Rhesus Gene Database: A data source to support the analysis of the effect of Fcγ-receptor polymorphisms in rhesus macaques on monoclonal antibody-mediated lymphocyte depletion and SIVmac251setpoint viremia.
Epstein-Barr virus T cell Antigen Database: A data source and analysis platform for EBV immune target discovery by focusing on EBV antigens that contain HLA ligands and T cell epitopes.
Human Papillomavirus T cell Antigen Database: A data source and analysis platform for HPV immune target discovery by focusing on HPV antigens that contain HLA ligands and T cell epitopes.
PREDmafa: A computational system for in silico identification of peptides binding to MHC alleles of cynomolgus macaques
ConeSnail Database: It facilitates the management of an ever-expanding conopeptides of marine cone snail venoms and represents a bioinformatics approach to this field, to further the development of conopeptides in human therapeutic applications.
MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
MULTIPRED version 1: a computational system for prediction of promiscuous HLA binding peptides
PREDBALB/c: a system for prediction of peptide binding to the H2d molecules, a haplotype of the BALB/c mouse
Hotspot Hunter: A computational system for prediction and analysis of T-cell epitope hotspots
PREDTAP: A computational system that predicts peptides binding to the Transporters associated with Antigen Processing (TAP).
PREDNOD: A system that predicts peptides binding to major histocompatibility complex haplotype (H2-Ag7,H2-Db,H2-Kd) of the non-obese diabetic (NOD) mouse – an animal model for insulin-dependent diabetes mellitus (IDDM).
5′-end Information Extraction (FIE): A system for the extraction of genomic DNA sequences around the start and translation initiation site of human genes
ICE (Information for the Coordinates of Exons): A Human Splice Sites Database
Invited Talks
“Prediction of promiscuous peptides that bind HLA class I molecules.” 1st Saudi Arabia Bioinformatics Workshop, Riyadh, Saudi Arabia, February 2006.
“Prediction of promiscuous peptides that bind HLA class I molecules.” Pre-8th FAOBMB Symposium Satellite Workshop on Bioinformatics, Lahore, Pakistan, November 2005.
Oral Presentations and Demos
“MULTIPRED: a computational system for prediction of promiscuous HLA class I binders.” 3rd Asia-Pacific Bioinformatics Conference (APBC2005) Graduate Student Satellite Symposium. NTU, Singapore, January 2005.
Software Demo on MULTIPRED. 3rd Asia-Pacific Bioinformatics Conference (APBC2005), Singapore, January 2005.
“Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens. “12th International Conference on Intelligent Systems for Molecular Biology, with the European Conference on Computational Biology (ISMB/ECCB2004). Glasgow, Scotland, August 2004.
“Predictive Modeling of Viral T cell Epitopes.” Singapore Immunoinformatics Symposium: From Databases to Vaccines, March 1, 2004.
“Neural models for predicting viral vaccine targets.” Conference on Neuro-Computing and Evolving Intelligence 2003 (NCEI 2003). Auckland, New Zealand, November 2003.
Grants
“Development of Next-Generation Immunogenicity Prediction Tools.” Pfizer, $150,000. Vladimir Brusic (Principal Investigator) and Guanglan Zhang (Co-Investigator)
Faculty Q&A
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 healthcare 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.
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.
What advice do you have for new students?
For students who want to enter into health informatics, I congratulate them. It’s a relatively new field, so there are very good career prospects for the future.