Yves A. Lussier, MD, FAMCI, is the Chair of Biomedical Informatics at University of Utah School of Medicine. As a professional engineer and physician-scientist, he 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.
Dr. Lussier comes to the University Utah from the University of Arizona, where he was the Associate Vice President for Information Science and Chief Knowledge Officer of the UArizona Health Sciences (UAHS), Founding Director of the Center for Biomedical Informatics and Biostatistics, and Professor of Medicine. During his time at UArizona, he developed novel programs in biomedical informatics, computational genomics, and precision health, as well as provided critical leadership to advance precision health approaches to health outcomes and healthcare delivery and in the development of big data analytical tools and resource services. Prior to his tenure at UArizona, Dr. Lussier was professor of medicine, bioengineering and biopharmaceutical sciences at University of Illinois at Chicago (UIC), and assistant vice president for health affairs and chief research information officer for the University of Illinois Hospital and Health Sciences System (2011-13). From 2006-2011, he was the 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. From 2001-2006, Dr. Lussier was an assistant professor in the Departments of Biomedical Informatics and Medicine at Columbia University in New York.
Lussier’s research group conducts pioneering hypothesis-driven computational modeling predictions in precision medicine that are then validated in vitro, in vivo and in clinical trials. As a leader of the fields of translational bioinformatics and of Data Science-augmented precision medicine, he has launched successful companies and international conferences, authored 185 publications, and delivered more than 100 invited presentations in precision medicine, systems medicine, and translational bioinformatics, including 28 opening keynotes at international conferences. He has been awarded $190,000,000 in grants as principal, core leader, or co-investigator, and mentored or co-mentored over 90 graduates, post graduate fellows, and junior faculty members, of which twelve are faculty members, seven obtained K-awards, and five obtained R awards. Dr. Lussier’s honors include three IBM Faculty Awards, inducted Fellow of the American College of Medical Informatics (ACMI), 1st recipient of the Columbia University Faculty Mentoring Award, “Ambassador for Health Sciences” at the University of Sherbrooke (Canada), and 16 outstanding publication awards from the American Medical Informatics Association (AMIA), the International Society for Computational Biology (ISCB), and the Translational Bioinformatics Conference (TBC). In 2016, Dr. Lussier was invited among ten USA academic leaders invited by the White House for its Precision Medicine Summit.
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
Dillon Aberasturi is a PhD student in statistics at The University of Arizona with a Masters in Statistics. His previous research involved the connection between self-similar trees and hyperbolic space. His current research focuses on creating new methods for using N-of-1 studies to evaluate differences in disease mechanism in subpopulations and rare diseases.
Liam Wilson is an undergraduate at the University of Arizona pursuing degrees in mathematics and computer science, and is a research assistant with the Lussier Group. Liam started working with the Lussier Group as a high school junior as a part of the KEYS Research Internship at the UofA, and has continued with the group both during their time at the UA as well as the University of Utah. Liam’s contributions have been towards a variety of projects, from validating large-scale environmental exposure/disease predictions to extending single-subject methods developed for the transcriptome to other ‘omics. Liam is particularly interested in the efficiency facet of research programming and incorporating more traditional software engineering techniques in research settings to improve productivity and the usability of products of research.
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
|Joanne Berghout, PhD||Pfizer||Senior Computational Geneticist|
|Francesca Vitali, PhD||The University of Arizona||Research Assistant Professor|
|Samir Rachid Zaim, PhD||Code for Venezuela||biostatistician and Data Scientist|
|Jungwei Fan, PhD||Mayo Clinic||Researcher|
|Dillon Aberasturi||The University of Arizona||PhD Student|
|Qike Li, PhD||Quantiply||Data Scientist|
|A. Grant Schissler, PhD||University of Nevada, Reno||Assistant Professor|
|Ikbel Achour, PhD||MedImmune||Lead Scientist, Translational Medicine|
|Haiquan Li, PhD||The University of Arizona||Assistant Professor, Biosystems Informatics|
|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||Research Associate 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.
ICD-9-CM To ICD-10-CM Conversion Tool
Vol.3 ICD-9-CM to ICD-10-PCS Conversion Tool
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:
The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools. - JAMIA 2013 Editor Pick
COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling - AMIA Annu Symp Proc. 2014
Challenges and remediation for Patient Safety Indicators in the transition to ICD-10-CM. - JAMIA, 2015
Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM. - JAMIA 2015
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
Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study. Gardeux V*, Arslan AD*, Achour I*, Ho TT*, Beck WT, Lussier YA. - BMC Med Genomics. 2014.
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