Browse by Research Team

Advances in Neural Information Processing Systems

It is all in the noise: efficient multi-task Gaussian process inference with structured residuals.
Rakitsch, B., Lippert, C., Borgwardt, K. and Stegle, O.
In: 27th Annual Conference on Neural Information Processing Systems, 5-10 Dec 2013, Lake Tahoe, United States of America. December 2013

Efficient inference in matrix-variate Gaussian models with iid observation noise.
Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K.
Advances in Neural Information Processing Systems 24 : 1-9. 2011

American Journal of Human Genetics

Profiling of short-tandem-repeat disease alleles in 12,632 human whole genomes.
Tang, H., Kirkness, E.F., Lippert, C., Biggs, W.H., Fabani, M., Guzman, E., Ramakrishnan, S., Lavrenko, V., Kakaradov, B., Hou, C., Hicks, B., Heckerman, D., Och, F.J., Caskey, C.T., Venter, J.C. and Telenti, A.
American Journal of Human Genetics 101 (5): 700-715. 2 November 2017

Bioinformatics

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.
Lippert, C., Xiang, J., Horta, D., Widmer, C., Kadie, C., Heckerman, D. and Listgarten, J.
Bioinformatics 30 (22): 3206-3214. 15 November 2014

A powerful and efficient set test for genetic markers that handles confounders.
Listgarten, J., Lippert, C., Kang, E.Y., Xiang, J., Kadie, C.M. and Heckerman, D.
Bioinformatics 29 (12): 1526-1533. 15 June 2013

Detecting regulatory gene-environment interactions with unmeasured environmental factors.
Fusi, N., Lippert, C., Borgwardt, K., Lawrence, N.D. and Stegle, O.
Bioinformatics 29 (11): 1382-1389. 1 June 2013

A Lasso multi-marker mixed model for association mapping with population structure correction.
Rakitsch, B., Lippert, C., Stegle, O. and Borgwardt, K.
Bioinformatics 29 (2): 206-214. 15 January 2013

Gene function prediction from synthetic lethality networks via ranking on demand.
Lippert, C., Ghahramani, Z. and Borgwardt, K.M.
Bioinformatics 26 (7): 912-918. 1 April 2010

CEUR Workshop Proceedings

Separating sparse signals from correlated noise in binary classification.
Mandt, S., Wenzel, F., Nakajima, S., Lippert, C. and Kloft, M.
In: 2016 UAI Workshop on Causation: Foundation to Application, UAI-CFA, 29 June 2016, Jersey City, United States. 2016

Human Molecular Genetics

Deep learning of genomic variation and regulatory network data.
Telenti, A., Lippert, C., Chang, P.C. and DePristo, M.
Human Molecular Genetics 27 (R1): R63-R71. 1 May 2018

IEEE Transactions on Pattern Analysis and Machine Intelligence

Ensembles of lasso screening rules.
Lee, S., Görnitz, N., Xing, E.P., Heckerman, D. and Lippert, C.
IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (12): 2841-2852. 24 November 2017

Journal of Human Genetics

Quantifying the uncertainty in heritability.
Furlotte, N.A., Heckerman, D. and Lippert, C.
Journal of Human Genetics 59 (5): 269-275. May 2014

Journal of Machine Learning Research

A kernel method for unsupervised structured network inference.
Lippert, C., Stegle, O., Ghahramani, Z. and Borgwardt, K.M.
Journal of Machine Learning Research 5 : 368-375. 2009

Machine Learning

Sparse probit linear mixed model.
Mandt, S., Wenzel, F., Nakajima, S., Cunningham, J., Lippert, C. and Kloft, M.
Machine Learning 106 (9-10): 1621-1642. October 2017

NIPS workshop on Machine Learning For Healthcare

Finding sparse features in strongly confounded medical binary data.
Mandt, S., Wenzel, F., Nakajima, S., Cunningham, J., Lippert, C. and Kloft, M.
In: NIPS Workshop on Machine Learning in Healthcare. 2015

Nature Communications

Warped linear mixed models for the genetic analysis of transformed phenotypes.
Fusi, N., Lippert, C., Lawrence, N.D. and Stegle, O.
Nature Communications 5 : 4890. 19 September 2014

Nature Genetics

FaST-LMM-Select for addressing confounding from spatial structure and rare variants.
Listgarten, J., Lippert, C. and Heckerman, D.
Nature Genetics 45 (5): 470-471. May 2013

Whole-genome sequencing of multiple Arabidopsis thaliana populations.
Cao, J., Schneeberger, K., Ossowski, S., Guenther, T., Bender, S., Fitz, J., Koenig, D., Lanz, C., Stegle, O., Lippert, C., Wang, X., Ott, F., Mueller, J., Alonso-Blanco, C., Borgwardt, K., Schmid, K.J. and Weigel, D.
Nature Genetics 43 (10): 956-963. 28 August 2011

Nature Methods

Efficient set tests for the genetic analysis of correlated traits.
Casale, F.P., Rakitsch, B., Lippert, C. and Stegle, O.
Nature Methods 12 (8): 755-758. August 2015

Accurate liability estimation improves power in ascertained case-control studies.
Weissbrod, O., Lippert, C., Geiger, D. and Heckerman, D.
Nature Methods 12 (4): 332-334. April 2015

Epigenome-wide association studies without the need for cell-type composition.
Zou, J., Lippert, C., Heckerman, D., Aryee, M. and Listgarten, J.
Nature Methods 11 (3): 309-311. March 2014

Improved linear mixed models for genome-wide association studies.
Listgarten, J., Lippert, C., Kadie, C.M., Davidson, R.I., Eskin, E. and Heckerman, D.
Nature Methods 9 (6): 525-526. 30 May 2012

FaST linear mixed models for genome-wide association studies.
Lippert, C., Listgarten, J., Liu, Y., Kadie, C.M., Davidson, R.I. and Heckerman, .
Nature Methods 8 (10): 833-835. 4 September 2011

Nucleic Acids Research

Patterns of methylation heritability in a genome-wide analysis of four brain regions.
Quon, G., Lippert, C., Heckerman, D. and Listgarten, J.
Nucleic Acids Research 41 (4): 2095-2104. 1 February 2013

Plant Cell

easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies.
Grimm, D.G., Roqueiro, D., Salomé, P.A., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., Stegle, O., Schölkopf, B., Weigel, D. and Borgwardt, K.M.
Plant Cell 29 (1): 5-19. January 2017

Proceedings of the NIPS 2008 Workshop

Relation prediction in multi-relational domains using matrix factorization.
Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M. and Kriegel, H.P.
Proceedings of the NIPS 2008 Workshop 2008

Proceedings of the National Academy of Sciences of the United States of America

Identification of individuals by trait prediction using whole-genome sequencing data.
Lippert, C., Sabatini, R., Maher, M.C., Kang, E.Y., Lee, S., Arikan, O., Harley, A., Bernal, A., Garst, P., Lavrenko, V., Yocum, K., Wong, T., Zhu, M., Yang, W.Y., Chang, C., Lu, T., Lee, C.W.H., Hicks, B., Ramakrishnan, S., Tang, H., Xie, C., Piper, J., Brewerton, S., Turpaz, Y., Telenti, A., Roby, R.K., Och, F.J. and Venter, J.C.
Proceedings of the National Academy of Sciences of the United States of America 114 (38): 10166-10171. 19 September 2017

Scientific Reports

Further improvements to linear mixed models for genome-wide association studies.
Widmer, C., Lippert, C., Weissbrod, O., Fusi, N., Kadie, C., Davidson, R., Listgarten, J. and Heckerman, D.
Scientific Reports 4 : 6874. 12 November 2014

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics.
Lippert, C., Quon, G., Kang, E.Y., Kadie, C.M., Listgarten, J. and Heckerman, D.
Scientific Reports 3 : 1815. 9 May 2013

An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data.
Lippert, C., Listgarten, J., Davidson, R.I., Baxter, J., Poon, H., Kadie, C.M. and Heckerman, D.
Scientific Reports 3 : 1099. 22 January 2013

XRDS: Crossroads, The ACM Magazine for Students

Computational and statistical issues in personalized medicine.
Lippert, C. and Hackerman, D.
XRDS: Crossroads, The ACM Magazine for Students 21 (4): 24-27. 27 July 2015

bioRxiv

No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data".
Lippert, C., Sabatini, R., Maher, M.C., Kang, E.Y., Lee, S., Arikan, O., Harley, A., Bernal, A., Garst, P., Lavrenko, V., Yocum, K., Wong, T.M., Zhu, M., Yang, W.Y.n, Chang, C., Hicks, B., Ramakrishnan, S., Tang, H., Xie, C., Brewerton, S., Turpaz, Y., Telenti, A., Roby, R.K., Och, F. and Venter, J.C.
bioRxiv : 187542. 19 October 2017

LIMIX: genetic analysis of multiple traits.
Lippert, C., Casale, F.P., Rakitsch, B. and Stegle, O.
bioRxiv : 003905. 22 May 2014

eLife

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control.
Bartha, I., Carlson, J.M., Brumme, C.J., McLaren, P.J., Brumme, Z.L., John, M., Haas, D.W., Martinez-Picado, J., Dalmau, J., López-Galíndez, C., Casado, C., Rauch, A., Günthard, H.F., Bernasconi, E., Vernazza, P., Klimkait, T., Yerly, S., O'Brien, S.J., Listgarten, J., Pfeifer, N., Lippert, C., Fusi, N., Kutalik, Z., Allen, T.M., Müller, V., Harrigan, P.R., Heckerman, D., Telenti, A. and Fellay, J.
eLife 2 : e01123. 29 October 2013