C. Lippert

发表

Relation Prediction in Multi-Relational Domains using Matrix Factorization

Volker Tresp, H. Kriegel, C. Lippert, 2008 .

Identification of individuals by trait prediction using whole-genome sequencing data

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 2017, Proceedings of the National Academy of Sciences.

Ensembles of Lasso Screening Rules

Eric P. Xing, Christoph Lippert, Nico Görnitz, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

D. Heckerman, Christian Widmer, J. Listgarten, 2014, Scientific Reports.

Epigenome-wide association studies without the need for cell-type composition

Martin J. Aryee, D. Heckerman, J. Listgarten, 2014, Nature Methods.

A kernel method for unsupervised structured network inference

Zoubin Ghahramani, Oliver Stegle, Karsten M. Borgwardt, 2009, AISTATS.

Whole-genome sequencing of multiple Arabidopsis thaliana populations

Karsten M. Borgwardt, K. Borgwardt, C. Lippert, 2011, Nature Genetics.

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies[OPEN]

Bernhard Schölkopf, Karsten M. Borgwardt, Christoph Lippert, 2016, Plant Cell.

LIMIX: genetic analysis of multiple traits

Christoph Lippert, Barbara Rakitsch, Oliver Stegle, 2014, bioRxiv.

FaST-LMM-Select for addressing confounding from spatial structure and rare variants

David Heckerman, Christoph Lippert, Jennifer Listgarten, 2013, Nature Genetics.

Warped linear mixed models for the genetic analysis of transformed phenotypes

Neil D. Lawrence, Christoph Lippert, Nicolo Fusi, 2014, Nature Communications.

Improved linear mixed models for genome-wide association studies

Eleazar Eskin, David Heckerman, Christoph Lippert, 2012, Nature Methods.

An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data

D. Heckerman, J. Listgarten, C. Kadie, 2013, Scientific Reports.

Accurate Liability Estimation Substantially Improves Power in Ascertained Case Control Studies

D. Geiger, D. Heckerman, C. Lippert, 2014 .

Genetic Analysis of Transformed Phenotypes

Neil D. Lawrence, Nicoló Fusi, C. Lippert, 2014, 1402.5447.

T wo-sample T esting U sing D eep L earning

M. Kloft, C. Lippert, Matthias Kirchler, 2019 .

A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data

K. Borgwardt, A. Honkela, C. Lippert, 2012, 1210.2850.

Correction

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 1975, Nature.

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics

David Heckerman, Carl M. Kadie, Christoph Lippert, 2013, Scientific Reports.

Profiling of Short-Tandem-Repeat Disease Alleles in 12,632 Human Whole Genomes

David Heckerman, Christoph Lippert, Amalio Telenti, 2017, American journal of human genetics.

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control

David Heckerman, Jacques Fellay, Christoph Lippert, 2013, eLife.

Two-sample Testing Using Deep Learning

M. Kloft, C. Lippert, Matthias Kirchler, 2019, AISTATS.

Iterative Patch Selection for High-Resolution Image Recognition

Aravindh Mahendran, C. Lippert, Benjamin Bergner, 2022, International Conference on Learning Representations.

Multimodal Self-Supervised Learning for Medical Image Analysis

Tassilo Klein, Christoph Lippert, Moin Nabi, 2019, IPMI.

Training Normalizing Flows from Dependent Data

M. Kloft, C. Lippert, Matthias Kirchler, 2022, 2209.14933.

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Samuel Kaski, C. Lippert, P. Marttinen, 2022, bioRxiv.

HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data

Alicia R. Martin, Samuel Kaski, C. Lippert, 2022 .

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2009, GCB 2009.

Sparse probit linear mixed model

Shinichi Nakajima, Marius Kloft, John P. Cunningham, 2015, Machine Learning.

Sparse Estimation in a Correlated Probit Model

J. Cunningham, M. Kloft, F. Wenzel, 2015, ArXiv.

HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis

Christoph Lippert, Benjamin Bergner, Tim Henning, 2020, bioRxiv.

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

David Heckerman, Christoph Lippert, Jennifer Listgarten, 2014, Bioinform..

Efficient set tests for the genetic analysis of correlated traits

Christoph Lippert, Barbara Rakitsch, Oliver Stegle, 2015, Nature Methods.

Deep learning of genomic variation and regulatory network data.

M. DePristo, A. Telenti, C. Lippert, 2018, Human molecular genetics.

CORRIGENDUM: An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data

David Heckerman, Carl M. Kadie, Christoph Lippert, 2013, Scientific Reports.

FaST linear mixed models for genome-wide association studies

Ying Liu, David Heckerman, Christoph Lippert, 2011, Nature Methods.

A powerful and efficient set test for genetic markers that handles confounders

David Heckerman, Christoph Lippert, Jennifer Listgarten, 2012, Bioinform..

Accurate liability estimation improves power in ascertained case-control studies

David Heckerman, Dan Geiger, Christoph Lippert, 2014, Nature Methods.

Correction

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 2021, Journal of the history of the neurosciences.

Correction

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 2020, The Clinical neuropsychologist.

Patterns of methylation heritability in a genome-wide analysis of four brain regions

David Heckerman, Christoph Lippert, Jennifer Listgarten, 2013, Nucleic acids research.

A Lasso multi-marker mixed model for association mapping with population structure correction

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2013, Bioinform..

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2013, NIPS.

Detecting regulatory gene-environment interactions with unmeasured environmental factors

Neil D. Lawrence, Oliver Stegle, Karsten M. Borgwardt, 2013, Bioinform..

3D Self-Supervised Methods for Medical Imaging

C. Lippert, Aiham Taleb, Benjamin Bergner, 2020, NeurIPS.

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics pdf

Christoph Lippert, Aiham Taleb, Remo Monti, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

C. Lippert, U. Ohler, S. Konigorski, 2021, Nature Communications.

Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout pdf

Christoph Lippert, Aiham Taleb, Yamen Ali, 2021, ArXiv.

Quantifying the uncertainty in heritability

David Heckerman, Christoph Lippert, D. Heckerman, 2014, Journal of Human Genetics.

Disentanglement and Local Directions of Variance

Christoph Lippert, Alexander Rakowski, C. Lippert, 2021, ECML/PKDD.

Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease pdf

Christoph Lippert, Stefan Konigorski, Shahryar Khorasani, 2018, ArXiv.

Generative multi-adversarial network for striking the right balance in abdominal image segmentation

Christoph Meinel, Janne J. Näppi, Mina Rezaei, 2020, International Journal of Computer Assisted Radiology and Surgery.

Multimodal Self-Supervised Learning for Medical Image Analysis pdf

Tassilo Klein, Christoph Lippert, Moin Nabi, 2019, IPMI.

Appendix for: 3D Self-Supervised Methods for Medical Imaging

C. Lippert, Aiham Taleb, Benjamin Bergner, 2020 .

High-resolution subgrid models: background, grid generation, and implementation

C. Lippert, A. Sehili, G. Lang, 2014, Ocean Dynamics.

Efficient inference in matrix-variate Gaussian models with \iid observation noise

Neil D. Lawrence, Joris M. Mooij, Oliver Stegle, 2011, NIPS.

Training Normalizing Flows from Dependent Data

M. Kloft, C. Lippert, Matthias Kirchler, 2022, ICML.

Iterative Patch Selection for High-Resolution Image Recognition

Aravindh Mahendran, C. Lippert, Benjamin Bergner, 2022, ICLR.

HIGH-RESOLUTION IMAGE RECOGNITION

Aravindh Mahendran, C. Lippert, Benjamin Bergner, 2023 .

Learning from population-scale medical imaging data without labels

C. Lippert, 2019 .

Kernelised Normalising Flows

C. Lippert, Matthias Kirchler, Eshant English, 2023, arXiv.org.

Gene function prediction from synthetic lethality networks via ranking on demand

Zoubin Ghahramani, Karsten M. Borgwardt, Christoph Lippert, 2010, Bioinform..

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties

C. Lippert, D. Reeb, Barbara Rakitsch, 2020, NeurIPS.

Separating Sparse Signals from Correlated Noise in Binary Classification

Shinichi Nakajima, Marius Kloft, Stephan Mandt, 2016, CFA@UAI.

Laplace approximated Gaussian process state-space models

C. Lippert, Barbara Rakitsch, J. Lindinger, 2022, UAI.

DCID: Deep Canonical Information Decomposition

C. Lippert, Alexander Rakowski, 2023, ECML/PKDD.

Accurate liability estimation improves power in ascertained case-control studies

D. Geiger, D. Heckerman, C. Lippert, 2014, Nature Methods.

FaST linear mixed models for genome-wide association studies

D. Heckerman, J. Listgarten, C. Kadie, 2011, Nature Methods.

Computational and statistical issues in personalized medicine

David Heckerman, Christoph Lippert, D. Heckerman, 2015, XRDS.

LIMIX: genetic analysis of multiple traits

C. Lippert, O. Stegle, F. P. Casale, 2014, bioRxiv.

Efficient set tests for the genetic analysis of correlated traits

C. Lippert, O. Stegle, F. P. Casale, 2015, Nature Methods.

An efficient group test for genetic markers that handles confounding

David Heckerman, Christoph Lippert, Jennifer Listgarten, 2012 .

Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis

Janne J. Näppi, Christoph Lippert, Christoph Meinel, 2020, Medical Imaging.

easyGWAS: An integrated interspecies platform for performing genome-wide association studies pdf

Bernhard Schölkopf, Oliver Stegle, Karsten M. Borgwardt, 2012, ArXiv.

Detecting affiliation in colaughter across 24 societies

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 2016 .

No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data”

Eun Yong Kang, Smriti R. Ramakrishnan, Seunghak Lee, 2017, bioRxiv.