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Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
(2023)arXivNormalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, by avoiding the representations from becoming collinear across the layers. However, results on mean-field theory of batch normalization also conclude that this benefit comes at the expense of exploding gradients in depth. Motivated by these two aspects of batch ...Working Paper -
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
(2023)arXivA prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effective offline RL. In the present paper, we tackle the problem of hidden confounding in the nonidentifiable setting. We ...Working Paper -
ResMiCo: increasing the quality of metagenome-assembled genomes with deep learning
(2022)bioRxivThe number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction ...Working Paper -
Mutant SF3B1 promotes PDAC malignancy through TGF-β resistance
(2022)bioRxivThe splicing factor SF3B1 is recurrently mutated in various tumors, including pancreatic ductal adenocarcinoma (PDAC). The impact of the hotspot mutation SF3B1K700E on the PDAC pathogenesis, however, remains elusive. Here, we demonstrate that Sf3b1K700E alone is insufficient to induce malignant transformation of the murine pancreas, but increases aggressiveness of PDAC if it co-occurs together with mutated KRAS and p53. We further demonstrate ...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 -
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 -
Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine
(2020)bioRxivTumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by ...Working Paper -
A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types
(2019)bioRxivMotivation Personalized medicine aims at combining genetic, clinical, and environmental data to improve medical diagnosis and disease treatment, tailored to each patient. This paper presents a Bayesian nonparametric (BNP) approach to identify genetic associations with clinical/environmental features in cancer. We propose an unsupervised approach to generate data-driven hypotheses and bring potentially novel insights about cancer biology. ...Working Paper -
Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance
(2019)bioRxivAlthough studies have shown that urban environments and mass-transit systems have distinct genetic profiles, there are no systematic studies of these dense, human/microbial ecosystems around the world. To address this gap in knowledge, we created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 58 cities, spanning 3,741 samples and 4,424 taxonomically-defined microorganisms collected for ...Working Paper