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The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support
(2020)medRxivRecent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient’s tumor, enables prognostic and predictive ...Working Paper -
AStarix: Fast and Optimal Sequence-to-Graph Alignment
(2020)bioRxivWe present an algorithm for the optimal alignment of sequences to genome graphs. It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A* paradigm with a novel domain-specific heuristic function that accounts for the upcoming sub-sequence in the query to be aligned, resulting in a provably optimal alignment algorithm called ...Working Paper -
Aligning Distant Sequences to Graphs using Long Seed Sketches
(2022)bioRxivSequence-to-graph alignment is an important step in applications such as variant genotyping, read error correction and genome assembly. When a query sequence requires a substantial number of edits to align, approximate alignment tools that follow the seed-and-extend approach require shorter seeds to get any matches. However, in large graphs with high variation, relying on a shorter seed length leads to an exponential increase in spurious ...Working Paper -
SCIM: Universal Single-Cell Matching with Unpaired Feature Sets
(2020)bioRxivMotivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences ...Working Paper -
SF3B1 promotes tumor malignancy through splicing-independent co-activation of HIF1α
(2020)bioRxivHeterozygous mutations in the splicing factor SF3B1 are frequently occurring in various cancers and drive tumor progression through the activation of cryptic splice sites in multiple genes. Recent studies moreover demonstrate a positive correlation between expression levels of wildtype SF3B1 and tumor malignancy, although the underlying mechanisms for this phenomenon remain elusive. Here, we report that SF3B1 acts as a coactivator for ...Working Paper -
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
(2021)arXivMarginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data, which may not be readily available. In this work, we present a scalable marginal-likelihood estimation method to select both hyperparameters and network architectures, based on the training data alone. Some hyperparameters can be estimated online during training, ...Working Paper -
Boosting Variational Inference With Locally Adaptive Step-Sizes
(2021)arXivVariational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational ...Working Paper -
On Disentanglement in Gaussian Process Variational Autoencoders
(2021)arXivComplex multivariate time series arise in many fields, ranging from computer vision to robotics or medicine. Often we are interested in the independent underlying factors that give rise to the high-dimensional data we are observing. While many models have been introduced to learn such disentangled representations, only few attempt to explicitly exploit the structure of sequential data. We investigate the disentanglement properties of ...Working Paper -
Early prediction of respiratory failure in the intensive care unit
(2021)arXivThe development of respiratory failure is common among patients in intensive care units (ICU). Large data quantities from ICU patient monitoring systems make timely and comprehensive analysis by clinicians difficult but are ideal for automatic processing by machine learning algorithms. Early prediction of respiratory system failure could alert clinicians to patients at risk of respiratory failure and allow for early patient reassessment ...Working Paper -
SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing
(2021)bioRxivRecently developed single-cell DNA sequencing technologies enable whole-genome, amplification-free sequencing of thousands of cells at the cost of ultra-low coverage of the sequenced data (< 0.05x per cell), which mostly limits their usage to the identification of copy number alterations (CNAs) in multi-megabase segments. Aside from CNA-based subclone detection, single-nucleotide variant (SNV)-based subclone detection may contribute ...Working Paper