Welcome to the Biomedical Informatics Lab of Prof. Dr. Gunnar Rätsch

The research in our group lies at the interface between methods research in Machine Learning, Genomics and Medical Informatics and relevant applications in biology and medicine.

We develop new analysis techniques that are capable of dealing with large amounts of medical and genomic data. These techniques aim to provide accurate predictions on the phenomenon at hand and to comprehensibly provide reasons for their prognoses, and thereby assist in gaining new biomedical insights.

Current research includes a) Machine Learning related to time-series analysis and iterative optimization algorithms, b) methods for transcriptome analyses to study transcriptome alterations in cancer, c) developing clinical decision support systems, in particular, for time series data from intensive care units, d) new graph genome algorithms to store and analyze very large sets of genomic sequences, and e) developing methods and resources for international sharing of genomic and clinical data, for instance, about variants in BRCA1/2.

Abstract Cancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

Authors Claudia Calabrese, Kjong-Van Lehmann, Lara Urban, Fenglin Liu, Serap Erkek, Nuno Fonseca, Andre Kahles, Leena Helena Kilpinen-Barrett, Julia Markowski, PCAWG-3, Sebastian Waszak, Jan Korbel, Zemin Zhang, Alvis Brazma, Gunnar Raetsch, Roland Schwarz, Oliver Stegle

Submitted bioRxiv

Link DOI

Abstract Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from 'serialised' MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data.

Authors Stephanie L Hyland, Cristobal Esteban, Gunnar Rätsch

Submitted arXiv

Link

Abstract Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe (FW) algorithms regained popularity in recent years due to their simplicity, effectiveness and theoretical guarantees. MP and FW address optimization over the linear span and the convex hull of a set of atoms, respectively. In this paper, we consider the intermediate case of optimization over the convex cone, parametrized as the conic hull of a generic atom set, leading to the first principled definitions of non-negative MP algorithms for which we give explicit convergence rates and demonstrate excellent empirical performance. In particular, we derive sublinear (O(1/t)) convergence on general smooth and convex objectives, and linear convergence (O(e−t)) on strongly convex objectives, in both cases for general sets of atoms. Furthermore, we establish a clear correspondence of our algorithms to known algorithms from the MP and FW literature. Our novel algorithms and analyses target general atom sets and general objective functions, and hence are directly applicable to a large variety of learning settings.

Authors Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi

Submitted NIPS 2017

Link DOI

Abstract Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database.

Authors Kana Shimizu, Koji Nuida, Gunnar Rätsch

Submitted Bioinformatics (Oxford, England)

Link Pubmed DOI

Abstract Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.

Authors Andre Kahles, Cheng Soon Ong, Yi Zhong, Gunnar Rätsch

Submitted Bioinformatics (Oxford, England)

Link Pubmed DOI