Yves A. Lussier, MD, FAMCI, is a UA professor of medicine; associate vice president for health sciences and chief knowledge officer for UAHS; associate director for cancer informatics and precision health for the University of Arizona Cancer Center; and associate director, BIO5 informatics, for the UA BIO5 Institute.
Dr. Lussier a professional engineer and physician-scientist is an international expert in translational bioinformatics and a pioneer in research informatics techniques including systems biology, data representation through ontologies and high-throughput methods in personalized medicine.
At the UA, he is leading efforts to fully develop novel programs in biomedical informatics, computational genomics and precision health. Dr. Lussier provides critical leadership in efforts to advance precision health approaches to health outcomes and healthcare delivery and in the development of big data analytical tools and resource services in support of the University’s clinical research and service missions.
Dr. Lussier comes to UA from the University of Illinois at Chicago (UIC), where he was professor of medicine, bioengineering and biopharmaceutical sciences, and assistant vice president for health affairs and chief research information officer for the University of Illinois Hospital and Health Sciences System. Prior to his tenure at UIC, Dr. Lussier was associate director of informatics for the University of Chicago Comprehensive Cancer Center as well as co-director of biomedical informatics for the Clinical and Translational Science Award (CTSA)-funded Institute for Translational Medicine (2006-2011). From 2001-2006, Dr. Lussier was an assistant professor in the Departments of Biomedical Informatics and Medicine at Columbia University in New York.
Dr. Lussier’s research interests focus on the use of ontologies, knowledge technologies and genomic network model to accurately individualize the treatment of disease and to repurpose therapies. His research has been featured in the New York Times and the Wall Street Journal. He has authored 130 publications and delivered more than 100 invited presentations in precision medicine, systems medicine and translational bioinformatics, including 14 opening conference keynotes.
A Fellow of the American College of Medical Informatics, Dr. Lussier is a member of numerous governance, technology transfer, scientific and editorial boards, including the American Medical Informatics Association, International Society for Computational Biology, Society for Clinical and Translational Science, American Society for Cancer Research, Healthcare Information and Management Systems Society, American Association of Pharmaceutical Scientists, American Association for the Advancement of Science and American Society for Human Genetics.
Dr. Lussier received a bachelor of engineering and his medical degree from the University of Sherbrooke, Quebec, Canada. He performed predoctoral research in the Departments of Medicine and Human Physiology at the University of Sherbrooke. After medical school, Dr. Lussier completed an internship in ophthalmology at Laval University Hospital in Quebec City, and a residency in family medicine at the University of Sherbrooke Medical Center. He was a post-doctoral residential fellow in the Department of Biomedical Informatics in the College of Surgeons & Physicians at Columbia University.
Jianrong Li has been worked in the Biomedical Informatics field for over 15 years, with several years in the banking industry. He has hands-on skills in I2B2, Bioinformatics, Natural Language Process, Data Mining, Artificial Intelligence, Biotechnology
Clinical Trials, Text Mining, Information Retrieval, and Statistics.
Years of Experience: 30 years
Technology: DB2, MySQL, Microsoft SQL Server, Solaris, Linux, Microsoft Windows (XP, 7, 8), Cytoscape
Programming Languages: PERL, Java, C, R, C#
Certification: CLARITY DATA MODEL - PROFESSIONAL BILLING
After completing her Bachelor's Degree and Master's Degree in Biomedical Engineering at the University of Pavia, Dr. Vitali received her PhD in Bioinformatics and Biomedical Engineering from the University of Pavia, Italy. Before joining the Lussier Research Group, she was a postdoctoral researcher at the Bioinformatics, Mathematical Modelling and Synthetic Biology (BMS) Lab at the University of Pavia under the mentorship of Prof. Riccardo Bellazzi. Her work mainly focused on developing computational methods to support drug repurposing and polypharmacology. During her career, Dr. Vitali developed strong programming experience with different languages and solid knowledge of data mining, statistics, graph theory, machine learning, and data integration techniques. One of the key aspects in these methods is their flexibility to make them suitable for use in different contexts. Her research was conducted in an expert multi¬disciplinary collaborative team, which included collaborations with international laboratories and pharmaceutical industries such as Sanofi and AstraZeneca.
Jungwei received his Ph.D. in Biomedical Informatics from Columbia University. His dissertation was on reciprocal solutions to make biomedical terminology/ontology and natural language process (NLP) benefit each other. In one direction, he developed NLP methods that used context- and content-based features to audit semantic classification of the Unified Medical Language System (UMLS) concepts. In the other direction, he developed methods of using the UMLS semantic information to improve NLP tasks such as word sense disambiguation (WSD) and syntactic parsing. Two of the derived journal articles were selected as best papers in Yearbook of Medical Informatics by the International Medical Informatics Association. After completing Ph.D. in 2010, Jungwei joined Kaiser Permanente Southern California as an informatics scientist to work with real world data and requirements in healthcare industry. He spent six years with hands-on architecting and developing a full-fledged, scalable NLP pipeline based on the Unstructured Information Management Architecture (UIMA). The pipeline generated significant value through applications in computer-assisted medical coding and preventive medicine. In addition to operational duties, he continued research along several tracks: 1) applied NLP for preventive medicine of cardiovascular diseases, 2) investigated technical benefits and issues in cross-institution NLP collaboration, 3) developed syntactic annotation guidelines with emphasis on handling ungrammatical clinical sentences, and 4) used NLP and big data technology to identify health issues in massive consumer product reviews. In 2016, Jungwei joined the Center for Biomedical Informatics & Biostatistics at the University of Arizona to contribute his clinical informatics skills for precision medicine and health disparity research. His current endeavor is on innovating solutions that use electronic medical records and genomic data to facilitate clinical research. The active projects involve investigation into comorbidities and methods of case-based reasoning.
Grant Schissler is a PhD candidate in statistical informatics through the graduate interdisciplinary program (GIDP) in statistics. His role is to develop statistical methodology to enable precision medicine. His projects involve single-subject analytics of 'omics experiments, including RNA-seq and single-cell RNA-seq of cancer samples. His other area of research is to enable practice-based reasoning through mining and analysis of big clinical data for rapid patient care. He enjoys working in this fast-paced, motivated, and interdisciplinary environment alongside other statisticians, physicians, biologists, geneticists, and computer scientists.
Qike Li is a fourth year graduate student in Statistics Graduate Interdisciplinary Progam (GIDP) at the University of Arizona. His research is in the field of translational statistics focusing on developing statistical theories and tools with an application in precision medicine. His current projects involve N-of-1 pathway and identifying differentially expressed genes between two transcriptomes.
Joanne Berghout, PhD is a Research Assistant Professor of Biomedical Informatics in the research group of Dr. Yves Lussier. Dr. Berghout received her PhD in Biochemistry from McGill University in Montreal, QC where she researched the genetics of complex traits and susceptibility to infectious disease in humans and mouse models. Following that, she spent three years as the Outreach Coordinator for the Mouse Genome Informatics (MGI) database in Bar Harbor, ME. There, she trained researchers in genetics, genomics, data structures and data mining to answer biological questions, and worked closely with other members of the MGI group to develop and optimize the MGI resource. Dr. Berghout’s research interests include genetics of all kinds, personalized medicine, big data, and scientific communication.
Colleen Kenost currently has two roles in the Lussier Lab which span the Department of Medicine and the Center for Biomedical Informatics and Biostatistics. Colleen’s main role is to bridge the gap between research and administration and to foster collaborative efforts between multiple units across campus and with external stakeholders. She currently serves as the both CB2 Director of Strategic Operations and the Multidisciplinary Knowledge Management Specialist. In these roles, she oversees all collaborative efforts, both operationally and administratively. She works across traditional boundaries, and is well-versed in translating research prerogatives into action and operationalizing strategic plans.
She received her Bachelors Degree in Business Administration and Masters Degree in Human Resource Development from Northeastern Illinois University. She has currently completed her coursework and is working on her dissertation for her Doctorate of Education in Interdisciplinary Leadership, with a concentration in non-profit social entrepreneurship, at Governors State University. She has been with The University of Arizona since 2013. Prior to this, she worked with Dr. Lussier at the University of Illinois at Chicago from 2011–2013. She also worked in the Departments of Medicine and Otolaryngology at the University of Illinois at Chicago from 2007-11.
Nima Pouladi is in his second year as a postdoctoral research fellow. His primary research goal is to combine the results from different ‘omics data in order uncover the hidden links among diseases and ultimately translate the findings to clinical practice, such as through the repurposing the available treatment options for various diseases
|Ikbel Achour, PhD||MedImmune||Lead Scientist, Translational Medicine|
|Haiquan Li, PhD||The University of Arizona||Assistant Professor, Medicine|
|Nima Pouladi, MD, PhD||The University of Arizona, Center for Biomedical Informatics and Biostatistics||Computational Researcher in Genomics|
|Michael Cantor||New York School of Medicine||Director of Clinical Research Informatics and Associate Professor|
|Younghee Lee||University of Utah||Assistant Professor, Biomedical Informatics Research|
|David J. Mann||NorthShore University HealthSystem||Dermatologist|
|Xinan Yang||The University of Chicago||Assistant Professor|
|James L. Chen||Ohio State University||Assistant Professor, Internal Medicine & Biomedical Informatics|
|Vincent Gardeux||EPFL Laboratory of Systems Biology & Genetics||Research Collaborator|
|Young Ji Lee||University of Pittsburgh||Assistant Professor|
|Peter LoPresti||University of Illinois at Chicago||MD Student|
|Alan Perez-Rathke||University of Illinois at Chicago||MD/PhD Graduate Student|
|Gurunadh Parinandi||AIM Specialty Health||Business Information Analyst 2|
|Kelly Regan||Ohio State University||NIH National Laboratory of Medicine Biomedical Informatics Research Training Program Fellow|
|Kanix Wang||University of Chicago||PhD Candidate|
|Ying Tao||Beijing Laboratory||IBM Researcher|
|Indra Neil Sarkar||University of Vermont||Director of Biomedical Informatics and Assistant Professor of Micorbiology and Molecular Genetics|
|Matthew Crowson||Duke University||OHNS Resident|
|Lee Sam||University of Michigan||PhD Candidate|
|Spiro Pantazatos||Columbia University, Dept. of Psychiatry||Assistant Professor of Clinical Neurobiology|
In response to the national Precision Medicine Initiative (PMI), UAHS has committed significant resources to expand the clinical utility of its open-source, patient-centric analytic methods, such as the N-of-1-pathways software, which aids physicians in interpreting the dynamic changes of disease-associated gene expression arising from patients’ own DNA blueprints. As part of the initiative, UAHS will translate large-scale clinical and genomic data into actionable individual outcomes through two of its centers: the UA Center for Biomedical Informatics and Biostatistics (CB2) and the UA Center for Applied Genetics and Genomic Medicine (TCAG2M). Both centers bring together physicians, scientists, patients and other key stakeholders to develop strategies that advance understanding of the factors contributing to individual health and disease and personalized approaches to disease prevention, early detection and treatment.
Yves Lussier, MD, FACMI, will lead the UAHS patient-centric analytical methods and was invited to attend the Precision Medicine Initiative Summit held Feb 25th in Washington, D.C. UAHS’ involvement in the PMI was initiated and facilitated by Ikbel Achour, PhD, who serves as CB2’s director for precision health. Dr. Lussier collaborates closely with Kenneth S. Ramos, MD, PhD, PharmB, associate vice president for precision health sciences, director of the UA TCAG2M and an elected member of the National Academy of Medicine.
About: Automatically identify errors from ICD-9-CM to CD-10-CM code transition that potentially can disrupt billing and clinical practice.
Goal: Clarify and quantify the administrative and financial impact from ICD-10-CM implementation in clinical datasets to reduce inaccuracies and reporting errors.
Impact. ICD-9 to ICD-10 transition based on:
In this era of precision medicine, accurate personal transcriptome interpretation and N-of-1 (single-subject) efficacy trials remain unmet challenges. We therefore developed a method, “N-of-1-pathways,” that translates gene expression data profiles into disease mechanism significance for a pair of samples - one patient at the time.
The emergence of precision medicine ushered in the opportunity to incorporate individual molecular data into patient care. In contrast to personal DNA sequencing profiling increasingly pursued in clinical practice, genome-wide transcriptome profiling has often provided biological information at the gene and pathway levels that are common and applicable only to a larger cohort. The N-of-1-pathways method, a global framework, relies on three principles: i) the statistical universe is a single patient/sample; ii) significance is derived from genesets/biomodules; and iii) similarity metric of inter-mechanisms’ relationships. N-of-1-pathways provides a unique and novel framework for N-of-1-studies (e.g. patients, cell-lines, tissues, etc.) aimed at predicting individual response to therapy and biomarker discovery. N-of-1-pathways offers opportunities to include patient-centered “omics” reports into electronic medical records for individualized clinical interpretation and precise treatment.
'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine. Gardeux V*, Achour I*, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA. - J Am Med Inform Assoc. 2014
JAMIA Editor Pick 2014 - AWARD Best TBC Research Paper 2013; Presentation at Translational Bioinformatics Conference (TBC) - TBI AMIA 2013 Joint Summits on Translational Science
Presentation at Translational Bioinformatics Conference (TBC)
Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival. Schissler AG*, Gardeux V*, Li Q*, Achour I*, Li H, Piegorsch WW, Lussier YA. - Bioinformatics. 2015
Presentation at Intelligent Systems for Molecular Biology, ISMB 2015
Towards a PBMC "virogram assay" for precision medicine: Concordance between ex vivo and in vivo viral infection transcriptomes. Gardeux V, Bosco A, Li J, Halonen MJ, Jackson D, Martinez FD, Lussier YA. - J Biomed Inform. 2015
* These authors contributed equally to the work
Haiquan Li*, Ikbel Achour*, Lisa Bastarache*, Joanne Berghout, Vincent Gardeux, Jianrong Li,Younghee Lee, Lorenzo Pesce7, Xinan Yang, Kenneth S Ramos, Ian Foster, Joshua C Denny, Jason H Moore and Yves A Lussier.
* These authors contributed equally to this work
Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR 0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR412). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P = 0.046), bladder cancer (entropy P = 0.039), and rheumatoid arthritis (PheWAS case–control Po10− 4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.
npj Genomic Medicine (2016) 1, 16006; doi:10.1038/npjgenmed.2016.6; published online 27 April 2016