Reconciling Utility with Privacy in Genomics

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Bayesian Optimization for Chemical Reactions

Philippe Schwaller, Jeff Guo, Bojana Rankovic

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

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Identifying genetic and dietary modulators of metabolic disorders using systems genetics

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Long-term consumption of lipid-rich foods can contribute to common metabolic diseases and systemic low-grade inflammation. However, dietary responses and the development of non-communicable diseases are shaped by genetic factors and gene-by-environment int ...

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Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

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Chromatin modules and their implication in genomic organization and gene regulation

Bart Deplancke, Guido Van Mierlo, Judith Franziska Kribelbauer

Regulation of gene expression is a complex but highly guided process. While genomic technologies and computational approaches have allowed high-throughput mapping of cis-regulatory elements (CREs) and their interactions in 3D, their precise role in regulat ...

ELSEVIER SCIENCE LONDON2023

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

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With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

EPFL2023

Genomic insights into the coupling of a Chlorella-like microeukaryote and sulfur bacteria in the chemocline of permanently stratified Lake Cadagno

Christel Sybille Hassler

Meromictic Lake Cadagno is a permanently stratified system with a persistent microbial bloom within the oxic-anoxic boundary called the chemocline. The association between oxygenic and anoxygenic photosynthesis within the chemocline has been known for at l ...

SPRINGERNATURE2023

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Michael Christoph Gastpar, Aditya Pradeep

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

2023

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Genomic insights into the coupling of a Chlorella-like microeukaryote and sulfur bacteria in the chemocline of permanently stratified Lake Cadagno

Christel Sybille Hassler

SPRINGERNATURE2023

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

2023

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Michael Christoph Gastpar, Aditya Pradeep

2023