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My interests are in
machine
learning and applied
statistics, especially
as applied to
large-scale, biologically-based problems
(this area lies within the broader field of
bioinformatics
/
computational biology).
Projects which aim to reveal new fundamental biological insight, as well as those
centered on more clinical applications both appeal to me.
My current biological focus, is, broadly, on the rational design of an HIV vaccine.
In this domain, I have tackled the
problems of: deducing HLA restrictions from Elispot assays which provide this information
only
indirectly; resolving ambiguity in HLA typing results in order to achieve high-resolution HLA data
when this was not actually generated in the laboratory; and in silico epitope prediction.
Solutions to these problems are also of broader practical importance, such as in vaccine design
and infectious disease research in general, and in transplant medicine. The biological focus
during my Ph.D. was a platform focus---that of Liquid Chromatography Mass Spectrometry (LCMS)
proteomics for the purposes of biomarker discovery. Prior to that, I worked
toward understanding genetic components of various cancers through SNP and microarray data.
Novel machine learning methodology developed throughout these projects includes: a class of
generative models for alignment and comparison of time series; generalization of False Discovery
Rates (FDR) to Graphical Model structure learning for pair wise entity extraction; a more accurate
method for phasing genotype data tailored to the HLA domain; fast and convex multi-task learning by
way of specialized features with logistic regression.
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Statistical resolution of ambiguous HLA typing data.
(abstract,
paper,
coverage in the magazine BioInform,
press release)
Jennifer Listgarten, Zabrina Brumme, Carl Kadie, Gao Xiaojiang, Bruce Walker, Mary
Carrington, Phillip Goulder, David Heckerman,
in PLoS Computational Biology, 2008, 4(2):e1000016
For the public web server tool based on this work, (and soon, also executables to run locally),
go
here .
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A statistical framework for modeling HLA-dependent T-cell response data.
(abstract, paper,
press release)
Jennifer Listgarten, Nicole Frahm, Carl Kadie, Christian Brander and David
Heckerman,
PLoS Computational Biology, 2007, 3(10):e188
Web tool, executable and source code available
here, under "HLA Assignment"
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Extensive HLA class I allele promiscuity among viral CTL epitopes.
(abstract)
N. Frahm, K. Yusim, T. Suscovich, S. Adams, J. Sidney, P. Hraber, H. Hewitt, CH. Linde, D. Kavanagh, T.
Woodberry, L. Henry, K. Faircloth, J. Listgarten, C. Kadie, N. Jojic, K. Sango, N. Brown, E. Pae, M. Zaman, F.
Bihl, A. Khatri, M. John, S. Mallal, F. Marincola, B. Walker, A. Sette, D. Heckerman, B. Korber, C. Brander
European Journal of Immunology, 2007 37(9):2419-2433.
See paper above for code/tools used in this paper.
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Evidence that dysregulated DNA mismatch repair characterizes human
non-melanoma skin cancer
(abstract)
Leah C. Young, Jennifer Listgarten, Martin J. Trotter, Susan E. Andrew, Victor A. Tron
British Journal of Dermatology, 2008 158(1):59-69.
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Determining the number of non-spurious arcs in a learned DAG model: Investigation of a
Bayesian and a frequentist approach.
(abstract,
paper)
Jennifer Listgarten and David Heckerman
Proceedings of Twenty-Third Conference on Uncertainty in Artificial
Intelligence, UAI Press, July 2007.
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Analysis of sibling time series data: alignment and difference
detection.
(abstract,
thesis and code)
Jennifer Listgarten,
Ph.D. Thesis, Department of Computer Science, University of Toronto, Dec. 2006.
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Bayesian detection of infrequent differences in
sets of time series with shared structure.
(abstract, paper)
Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin and Sean Cutler,
Advances in Neural Information Processing Systems 19, MIT Press,
Cambridge, MA, 2007 (
NIPS 2006).
Best Student Paper, Honorable Mention.
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Difference detection in LC-MS data for protein biomarker discovery.
(abstract, paper and data set)
Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Peter Wong and Andrew Emili,
Bioinformatics, 2007 23:e198-e204 [by way of
ECCB 2006 (European Conference on Computational Biology)]
Best Student Paper, 3rd prize
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Leveraging information across HLA alleles/supertypes improves epitope prediction.
(abstract,
paper)
David Heckerman, Carl Kadie, Jennifer Listgarten,
Journal of Computational Biology, 2007 14: 736-746
(shorter version also appears
Proceedings of Research in Computational Molecular Biology. Lecture
Notes in Computer Science, Volume 3909, Mar 2006, 296-308.)
(an yet an older version appears as Technical Report MSR-TR-05-127, Microsoft Research, September,
2005)
Web tool, executable and source code available
here, under "Epitope Prediction"
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Practical proteomic biomarker discovery: taking a step back to leap
forward.
(abstract)
(paper)
Jennifer Listgarten and Andrew Emili,
Drug Discovery Today, 2005 10:1697-1702.
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Statistical and computational methods for comparative proteomic profiling using
liquid chromatography-tandem mass spectrometry.
(abstract)
(paper)
Jennifer Listgarten and Andrew Emili,
Molecular and Cellular Proteomics, 2005 4:419-434.
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Multiple alignment of continuous time series.
(abstract, paper, slides, and audio demo)
Jennifer Listgarten, Radford M. Neal, Sam T. Roweis and Andrew Emili,
Advances in Neural Information Processing Systems 17, MIT Press, Cambridge, MA,
2005 (NIPS 2004).
The Continuous Profile Models (CPM) Matlab Toolbox
is available
here.
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Predictive models for breast cancer susceptibility from multiple,
single nucleotide polymorphisms.
(abstract)
(paper)
Jennifer Listgarten, Sambasivarao Damaraju,
Brett Poulin, Lillian Cook, Jennifer Dufour, Adrian Driga, John Mackey, David Wishart,
Russ Greiner and Brent Zanke,
Clinical Cancer Research 2004:10(8):2725-37.
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Clinically validated
benchmarking of normalization techniques for two-colour oligonucleotide
spotted microarray slides.
(abstract)
(paper)
Jennifer Listgarten, Kathryn Graham,
Sambasivarao Damaraju, Carol Cass, John Mackey and Brent Zanke,
Applied Bioinformatics 2003:2(4)219-228.
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Lymphovascular invasion is associated with poor survival in gastric
cancer: an application of gene-expression and tissue array techniques.
Bryan J. Dicken, Kathryn Graham, Stewart M. Hamilton, Sam Andrews,
Raymond Lai, Jennifer Listgarten, Gian S. Jhangri, L. Duncan Saunders,
Sambasivarao Damaraju and Carol E. Cass,
Annals of Surgery 2006: 243(1):64-73.
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Exploring qualitative probabilities for image
understanding.
(pdf 1.2MB)
(ps.gz 0.6MB)
Jennifer Listgarten,
M.Sc. Thesis, Department of Computer Science, University of Toronto, October 2000.
(Supervisor: Allan Jepson)
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