Publications: Computational Biology

 

·         D. Heckerman, D. Gurdasani, C. Kadie, C. Pomilla, T. Carstensen, H. Martin, K. Ekoru, R.N. Nsubuga, G. Ssenyomo A. Kamali, P. Kaleebu, C. Widmer, and M.S. Sandhu.  Linear mixed model for heritability estimation that explicitly addresses environmental variation.  PNAS, 113: 7377–7382, July 2016 (doi: 10.1073/pnas.1510497113).

 

·         C. Lippert and D. Heckerman.  Computational and statistical issues in personalized medicine.  XRDS 21, 24-27, Summer 2015 (doi:10.1145/2788502).

 

·         K. Pfafferott, P. Deshpande, E. McKinnon, S. Merani, A. Lucas, S. Mallal, D. Heckerman, M. John, S. Gaudieri, M. Lucas.  Anti-Hepatitis C virus T-cell immunity in the context of multiple exposures to the virus.  PLoS One, June 2015 (doi:10.1371/journal.pone.0130420).

 

·         C. Berger, A. Llano, J. Carlson, Z. Brumme, Mark Brockman, S. Cedeño, P.R. Harrigan, D. Kaufmann, D. Heckerman, A. Meyerhans, and C. Brander.  Immune screening identifies novel T cell targets encoded by antisense reading frames of HIV-1.  J. Virol 89, 4015-4019, April 2015 (doi:10.1128/JVI.03435-14).

 

·         G. Margarido and D. Heckerman. ConPADE: Genome Assembly Ploidy Estimation from Next-Generation Sequencing Data. PLoS Comput Biol 11(4): e1004229, April 2015 (doi:10.1371/journal.pcbi.1004229).

 

·         O. Weissbrod, C. Lippert, D. Geiger, and D. Heckerman.  Accurate liability estimation improves power in ascertained case-control studiesNature Methods, Feb 2015 (doi:10.1038/nmeth.3285).

 

·         C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman. Further Improvements to Linear Mixed Models for Genome-Wide Association Studies. Scientific Reports 4, 6874, Nov 2014 (doi:10.1038/srep06874).

 

·         F. Pereyra, D. Heckerman, J. Carlson, C. Kadie, D. Soghoian, D. Karel, A. Goldenthal, O. Davis, C. DeZiel, T. Lin, J. Peng, A. Piechocka, M. Carrington, and B. Walker. HIV Control Is Mediated in Part by CD8+ T-Cell Targeting of Specific Epitopes. J. Virol 88 12937-12948, Aug 2014.

 

·         C. Lippert, J. Xiang, D. Horta, C. Widmer, C.M. Kadie, D. Heckerman, J. Listgarten. Greater Power and Computational Efficiency for Kernel-Based Association Testing of Sets of Genetic Variants. Bioinformatics 30 , July 2014 (doi: 10.1093/bioinformatics/btu504).

 

·         J. Carlson, M. Schaefer, D. Monaco, R. Batorsky, D. Claiborne, J. Prince, M. Deymier, Z. Ende, N. Klatt, C. DeZiel, Tien-Ho Lin, J. Peng, A. Seese, R. Shapiro, J. Frater, T. Ndung’u, J. Tang, P. Goepfert, J. Gilmour, M. Price, W. Kilembe, D. Heckerman, P. Goulder, T. Allen, S. Allen, and E. Hunter. Selection bias at the heterosexual HIV-1 transmission bottleneck.  Science, 345(6193): 1254031, July 2014 (doi:10.1126/science.1254031).

 

·         N. Furlotte, D. Heckerman, and C. Lippert.  Quantifying the uncertainty in heritabilityJournal of Human Genetics 27, March 2014 (doi: 10.1038/jhg.2014.15).

 

·         J. Zou, C. Lippert, D. Heckerman, M. Aryee, and J. Listgarten.  Epigenome-wide association studies without the need for cell-type compositionNature Methods 11:309–311, Jan 2014 (doi:10.1038/nmeth.2815).

 

·         I. Bartha, J.Carlson, C. Brumme, P. McLaren, Z. Brumme, M. John, D. Haas, J. Martinez-Picado, J. Dalmau, C. López-Galíndez, C. Casado, A. Rauch, H. Günthard, E. Bernasconi, P. Vernazza, T. Klimkait, S. Yerly, S. O’Brien, J. Listgarten, N. Pfeifer, C. Lippert, N. Fusi, Z. Kutalik, T. Allen, V. Müller, P. Harrigan, D. Heckerman, A. Telenti, and J. Fellay.  A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral controleLife 2013;2:e01123, October 2013 (http://dx.doi.org/10.7554/eLife.01123).

 

·         C. Lippert, G. Quon, E.Y. Kang, C.M. Kadie, J. Listgarten, and D. Heckerman.  The benefits of selecting phenotype-specific variants for applications of mixed models in genomicsScientific Reports, 3, May 2013 (doi:10.1038/srep01815).

 

·         J. Listgarten, C. Lippert, E.Y. Kang, J. Xiang, C.M. Kadie, and D. Heckerman.  A powerful and efficient set test for genetic markers that handles confoundersBioinformatics, May 2013 (doi: 10.1093/bioinformatics/btt177).

 

·         J. Listgarten, C. Lippert, and D. Heckerman. FaST-LMM-Select for addressing confounding from spatial structure and rare variantsNature Genetics, 45: 470-471, April 2013 (doi:10.1038/ng.2620).

 

·         G. Quon, C. Lippert, D. Heckerman, and J. Listgarten.  Patterns of methylation heritability in a genome-wide analysis of four brain regionsNucleic Acids Research, March 2013 (doi: 10.1093/nar/gks1449).

 

·         C. Lippert, J. Listgarten, R.I. Davidson, J. Baxter, H. Poon, C.M. Kadie, and  D. Heckerman.  An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust DataScientific Reports, 3, Jan 2013 (doi:10.1038/srep01099).

 

·         J. Listgarten, C. Lippert, C.M. Kadie, R.I. Davidson, E. Eskin, and D. Heckerman.  Improved linear mixed models for genome-wide association studiesNature Methods, 9: 525-526, June 2012 (doi:10.1038/nmeth.2037).

 

·         X. Zhang, W. Cheng, J. Listgarten, C. Kadie, S. Huang, W. Wang, and D. Heckerman.  Learning transcriptional regulatory relationships using sparse graphical modelsPLoS One 7(5): e35762, May 2012 (doi:10.1371/journal.pone.0035762).

 

·         A. Renton, et al.  A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTDNeuron 72(2): 257-268, Oct 2011.

 

·         C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, and D. Heckerman.  FaST linear mixed models for genome-wide association studiesNature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681).

 

·         J. Listgarten, C. Kadie, E. Schadt, D. Heckerman.  Correction for hidden confounders in the genetic analysis of gene expression. PNAS, 107 (38): 16465-16470, September 2010 (doi: 10.1073/pnas.1002425107).

 

 ·         J. Carlson, Z. Brumme, C. Rousseau, C. Brumme, P. Matthews, C. Kadie, J. Mullins, B. Walker, P. Harrigan, P. Goulder, D. Heckerman.  Phylogenetic dependency networks: Inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Computational Biology, 4(11): e1000225, November 2008.

 

·         H. Kang, N. Zaitlen, C. Wade, A. Kirby, D. Heckerman, M. Daly, and E. Eskin, Efficient Control of Population Structure in Model Organism Association Mapping, Genetics, 178:1709-1723, March, 2008 (doi: 10.1534/genetics.107.080101).

 

·         J. Listgarten, Z. Brumme, C. Kadie, G. Xiaojiang, B. Walker, M. Carrington, P. Goulder, and D. Heckerman.  Statistical resolution of ambiguous HLA typing dataPLoS Computational Biology, 4(2): e1000016, February, 2008.

 

·         J. Listgarten, N. Frahm, C. Kadie, C. Brander, D. Heckerman.  A statistical framework for modeling HLA-dependent T cell response data, PLoS Computational Biology, 3(10): e188, October 2007.

 

·         D. Heckerman, C. Kadie, and J. Listgarten.  Leveraging information across HLA alleles/supertypes improves epitope prediction.  J. of Comp. Bio, 14(6): 736-746, August 2007.  Also appears as MSR-TR-05-127, Microsoft Research, September, 2005.

 

·         J. Carlson, C. Kadie, S. Mallal, and D. Heckerman.  Leveraging hierarchical population structure in discrete association studies. PLoS ONE, 2(7): e591, July 2007.

 

·         J. Listgarten and D. Heckerman, Determining the number of non-spurious arcs in a learned DAG model: Investigation of a Bayesian and a frequentist approach.  In Proceedings of Twenty Third Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, UAI Press, July 2007.  Also appears as Technical Report MSR-TR-07-60, Microsoft Research, May, 2007.  Local copy.

 

·         D. Nickle, M. Rolland, M. Jensen, S. Pond, W. Deng, M. Seligman, D. Heckerman, J. Mullins, and N. Jojic.  Coping with viral diversity in HIV vaccine design, PLoS Computational Biology, 3(4): e75, April 2007.

 

·         T. Bhattacharya, M. Daniels, D. Heckerman, B. Foley, N. Frahm, C. Kadie, J. Carlson, K. Yusim, B. McMahon, B. Gaschen, S. Mallal, J. Mullins, D. Nickle, J. Herbeck, C. Rousseau, G. Learn, T. Miura, C. Brander, B. Walker, B. Korber.  Founder effects in the assessment of HIV polymorphisms and HLA allele associations, Science, 315, 1583-1586, March 16 2007.

 

·         N. Jojic, V. Jojic, B. Frey, C. Meek, and D. Heckerman.  Using epitomes to model genetic diversity: Rational design of HIV vaccine cocktails.  NIPS 2005.

 

·         V. Jojic, N. Jojic, C. Meek, D. Geiger, A. Siepel, D. Haussler, and D. Heckerman: Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 161-168, 2004.  Also appears as Technical Report MSR-TR-2003-62, Microsoft Research, October, 2003.

 

·         N. Jojic, V. Jojic, and D. Heckerman, Joint discovery of haplotype blocks and complex trait associations from SNP sequences.  In Proceedings of Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, UAI Press, July 2004.

 

·         D. Heckerman and B. Nathwani.  Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inferenceMethods of Information in Medicine, 31:106-116, 1992.  Also in van Bemmel, J., McCray, A., editors, Yearbook of Medical Informatics, pages 430-440. International Medical Informatics Association, Rotterdam, The Netherlands, 1993.  Local copy.

 

·         D. Heckerman.  The certainty-factor model.  In S. Shapiro, editor, Encyclopedia of Artificial Intelligence, Second Edition, pages 131-138.  Wiley, New York, 1992.

 

·         D. Heckerman and E. Shortliffe.  From certainty factors to belief networksArtificial Intelligence in Medicine, 4:35-52, 1992.  Local copy.

 

·         D. Heckerman and B. Nathwani.  An evaluation of the diagnostic accuracy of PathfinderComputers and Biomedical Research, 25:56-74, 1992.  Local copy.

 

·         M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper.  Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base:  Part 1. The probabilistic model and inference algorithmsMethods of Information in Medicine, 30:241-255, 1991.

 

·         B. Middleton, M. Shwe, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper.  Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base:  Part 2.  Evaluation of diagnostic performanceMethods of Information in Medicine, 30:256-267, 1991.

 

·         D. Heckerman, E. Horvitz, and B. Middleton.  An approximate nonmyopic computation for value of information.  In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 135-141. Morgan Kaufmann, July 1991.  Local copy.

 

·         D. Heckerman.  Probabilistic Similarity Networks.  MIT Press, Cambridge, MA, 1991.  Review.

 

·         D. Heckerman.  Probabilistic similarity networks.  Networks, 20:607-636, 1990.

 

·         B. Nathwani, D. Heckerman, E. Horvitz, and T. Lincoln.  Integrated expert systems and videodisc in surgical pathology: An overview. Human Pathology, 21:11-27, 1990.

 

·         D. Heckerman.  Similarity networks for the construction of multiple-fault belief  networks.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 32-39. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Also in P. Bonissone, M. Henrion, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 6, pages 51-64.  North-Holland, New York, 1990.  Local copy.

 

·         H. Suermondt, G. Cooper, and D. Heckerman.  A combination of cutset conditioning with clique-tree propagation in the Pathfinder system.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 273-279. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Local copy.

 

·         D. Heckerman.  A tractable algorithm for diagnosing multiple diseases.  In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, pages 174-181. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1989.  Also in M. Henrion, R. Shachter, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 5, pages 163-171.  North-Holland, New York, 1990.  Local copy.

 

·         D. Heckerman, E. Horvitz, and B. Nathwani.  Update on the Pathfinder project.  In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, D, pages 203-207.  IEEE Computer Society Press, Silver Spring, MD, November 1989.  Local copy.

 

·         E. Horvitz, D. Heckerman, K. Ng, and B. Nathwani.  Heuristic abstraction in the decision-theoretic Pathfinder system.  In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, DC, pages 178-182. IEEE Computer Society Press, Silver Spring, MD, November 1989.

 

·         D. Heckerman.  An axiomatic framework for belief updates.  In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 123-128. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 11-22. North-Holland, New York, 1988.  Local copy.

 

·         D. Heckerman and E. Horvitz.  The myth of modularity in rule-based systems.  In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 115-121. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 23-34. North-Holland, New York, 1988.

 

·         D. Heckerman and R. Miller.  Towards a better understanding of the INTERNIST-1 knowledge base.  In Proceedings of Medinfo, Washington, DC, pages 27-31.  North-Holland, New York, October 1986.

 

·         E. Horvitz, D. Heckerman, and C. Langlotz.  A framework for comparing alternative formalisms for plausible reasoning.  In Proceedings AAAI-86 Fifth National Conference on Artificial Intelligence, Philadelphia, PA, pages 210-214. Morgan Kaufmann, San Mateo, CA, August 1986.  Local copy.

 

·         E. Horvitz, D. Heckerman, B. Nathwani, and L. Fagan.  The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesis-directed reasoning.  In Proceedings of Medinfo, Washington, DC, pages 27-31.  North-Holland, New York, October 1986.

 

·         E. Horvitz and D. Heckerman.  The inconsistent use of measures of certainty in artificial intelligence research.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 137-151. North-Holland, New York, 1986.

 

·         D. Heckerman.  Probabilistic interpretations for MYCIN's certainty factors.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA, pages 9-20. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985.  Also in L. Kanal. and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167-196. North-Holland, New York, 1986.