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 In this paper, we propose the first practical algorithm to minimize stochastic composite optimization problems over compact convex sets. This template allows for affine constraints and therefore covers stochastic semidefinite programs (SDPs), which are vastly applicable in both machine learning and statistics. In this setup, stochastic algorithms with convergence guarantees are either not known or not tractable. We tackle this general problem and propose a convergent, easy to implement and tractable algorithm. We prove $\mathcal{O}(k^{-1/3})$ convergence rate in expectation on the objective residual and $\mathcal{O}(k^{-5/12})$ in expectation on the feasibility gap. These rates are achieved without increasing the batchsize, which can contain a single sample. We present extensive empirical evidence demonstrating the superiority of our algorithm on a broad range of applications including optimization of stochastic SDPs.

Authors Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher

Submitted ArXiv

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Abstract The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.

Authors Melissa S. Cline , Rachel G. Liao , Michael T. Parsons , Benedict Paten , Faisal Alquaddoomi, Antonis Antoniou, Samantha Baxter, Larry Brody, Robert Cook-Deegan, Amy Coffin, Fergus J. Couch, Brian Craft, Robert Currie, Chloe C. Dlott, Lena Dolman, Johan T. den Dunnen, Stephanie O. M. Dyke, Susan M. Domchek, Douglas Easton, Zachary Fischmann, William D. Foulkes, Judy Garber, David Goldgar, Mary J. Goldman, Peter Goodhand, Steven Harrison, David Haussler, Kazuto Kato, Bartha Knoppers, Charles Markello, Robert Nussbaum, Kenneth Offit, Sharon E. Plon, Jem Rashbass, Heidi L. Rehm, Mark Robson, Wendy S. Rubinstein, Dominique Stoppa-Lyonnet, Sean Tavtigian, Adrian Thorogood, Can Zhang, Marc Zimmermann, BRCA Challenge Authors , John Burn , Stephen Chanock , Gunnar Rätsch , Amanda B. Spurdle

Submitted PLOS Genetics

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Abstract In recent years, the interest in \emph{unsupervised} learning of \emph{disentangled} representations has significantly increased. The key assumption is that real-world data is generated by a few explanatory factors of variation and that these factors can be recovered by unsupervised learning algorithms. A large number of unsupervised learning approaches based on \emph{auto-encoding} and quantitative evaluation metrics of disentanglement have been proposed; yet, the efficacy of the proposed approaches and utility of proposed notions of disentanglement has not been challenged in prior work. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than $\num{12000}$ models covering the six most prominent methods, and evaluate them across six disentanglement metrics in a reproducible large-scale experimental study on seven different data sets. On the positive side, we observe that different methods successfully enforce properties ``encouraged'' by the corresponding losses. On the negative side, we observe that in our study (1) ``good'' hyperparameters seemingly cannot be identified without access to ground-truth labels, (2) good hyperparameters neither transfer across data sets nor across disentanglement metrics, and (3) that increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. These results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.

Authors Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

Submitted ArXiv

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Abstract Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions (“neojunctions”) in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders (“putative neoantigens”).

Authors Andre Kahles, Kjong-Van Lehmann, Nora C. Toussaint, Matthias Hüser, Stefan Stark, Timo Sachsenberg, Oliver Stegle, Oliver Kohlbacher, Chris Sander, Gunnar Rätsch, The Cancer Genome Atlas Research Network

Submitted Cancer Cell

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Abstract The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

Authors Stephanie O. M. Dyke, Mikael Linden, […], Gunnar Rätsch, […], Paul Flicek

Submitted European Journal of Human Genetics

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