Yves A Lussier
Research Summary / Selected Publications
The Lussier Research Group is conducting hypothesis-driven translational research in biomedical informatics that focuses on the use of knowledge technologies to accurately individualize the understanding, prediction and treatments of diseases. More specifically, he has developed computational methods that bring together molecular bioinformatics, ontologies, natural language processing, and heterogeneous data integration to analyze an increasingly large and complex wealth of textual and semi-structured phenotypic, clinical, genomic, and molecular databases. Using phenomic- and systems biology approaches, his team has recently predicted: (i) a novel tumor suppressor gene and (ii) a network-targeting therapy to sensitize head and neck cancers resistant to anti-EGFR therapy, that have both been validated in vitro and in vivo by colleagues.
Lussier chaired the 2009 AMIA Summit on Translational Bioinformatics. He has established a track record in funding, building and leading teams that develop and “ translate” valuable leading-edge informatics solutions to clinical problems, within budget and ahead of schedule (e.g. Purkinje.com’s Dossier, New York Presbyterian Hospital’s Vigilens, Greene Lab’s Panmicrobial Array). Every year, th4e new York Presbyterian medical Center “Vigilens” monitors over 25 million laboratory results and sends alerts pertaining to 130 thousand critical events. Lussier’s team also co-designed the first comprehensive panmicrobial microarray for human diagnosis. In spite of its coverage of 1,200 distinct vertebrate viruses, it remains compact because less probes are required per organism as they have been designed in “essential protein domains” that are less likely to mutate (NAR. 2008). Since 2003, has established the diagnosis of a patient that eluded traditional methods of investigation (Emerg Infect Dis. 2007). The panmicrobial array also has served to rule out known human pathogens in the discovery of novel ones (NEJM. 2008).
The Lussier Research Group is conducting hypothesis-driven translational research in biomedical informatics that focuses on the use of knowledge technologies to accurately individualize the understanding, prediction and treatments of diseases. More specifically, he has developed computational methods that bring together molecular bioinformatics, ontologies, natural language processing, and heterogeneous data integration to analyze an increasingly large and complex wealth of textual and semi-structured phenotypic, clinical, genomic, and molecular databases. Using phenomic- and systems biology approaches, his team has recently predicted: (i) a novel tumor suppressor gene and (ii) a network-targeting therapy to sensitize head and neck cancers resistant to anti-EGFR therapy, that have both been validated in vitro and in vivo by colleagues.
Lussier chaired the 2009 AMIA Summit on Translational Bioinformatics. He has established a track record in funding, building and leading teams that develop and “ translate” valuable leading-edge informatics solutions to clinical problems, within budget and ahead of schedule (e.g. Purkinje.com’s Dossier, New York Presbyterian Hospital’s Vigilens, Greene Lab’s Panmicrobial Array). Every year, th4e new York Presbyterian medical Center “Vigilens” monitors over 25 million laboratory results...
Lussier YA; Rothwell DJ; Côté RA. (1998). "The SNOMED Model: A Knowledge Source for the Controlled Terminology of the Computerized Patient Record". Methods of Information in Medicine 37;160-164
Lussier YA, Williams R, Jalan S, Borlawsky T, Li J, Stern E. Partitioning Knowledge Bases Between Advanced Notification and Clinical Decision Support Systems. (accepted for the special issue on "Decision Support in Medicine" of the journal “Decision Support Systems”).
Cantor MN, Sarkar IN, Bodenreider O, Lussier YA. (2005). Genestrace: Phenomic Knowledge Discovery Via Structured Terminology. Pac Symp Biocomput. 103-14
Tao Y, Friedman C, Lussier YA. (2005). “Visualizing information across multidimensional post-genomic structured and textual databases” Bioinformatics. 15;21(8):1659-67. Epub 2004 Dec 14
Lussier YA, Rappaport D, Borlawsky T, Friedman C. (2006). PhenoGO: a Multistrategy Language Processing System Assigning Phenotypic Context to Gene Ontology Annotations. Pacific Symposium on Biocomputing 64-75
Lussier YA; Rothwell DJ; Côté RA. (1998). "The SNOMED Model: A Knowledge Source for the Controlled Terminology of the Computerized Patient Record". Methods of Information in Medicine 37;160-164
Lussier YA, Williams R, Jalan S, Borlawsky T, Li J, Stern E. Partitioning Knowledge Bases Between Advanced Notification and Clinical Decision Support Systems. (accepted for the special issue on "Decision Support in Medicine" of the journal “Decision Support Systems”).
Cantor MN, Sarkar IN, Bodenreider O, Lussier YA. (2005). Genestrace: Phenomic Knowledge Discovery Via Structured Terminology. Pac Symp Biocomput. 103-14
Tao Y, Friedman C, Lussier YA. (2005). “Visualizing information across multidimensional post-genomic structured and textual databases” Bioinformatics. 15;21(8):1659-67. Epub 2004 Dec 14
Lussier YA, Rappaport D, Borlawsky T, Friedman C. (2006). PhenoGO: a Multistrategy Language Processing System Assigning Phenotypic Context to Gene Ontology Annotations. Pacific Symposium on Biocomputing 64-75
