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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Effects of a two-day intensive OAT intake on the gut microbiome in participants with metabolic syndrome

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Clinical Nutrition ESPEN 2023年 54卷 717-718页

作者： Klümpen, L. Mantri, A. Seel, W. Stoffel-Wagner, B. Coenen, M. Nöthen, M. Krawitz, P. Stehle, P. Simon, M.-C. Nutrition and Microbiota Institute of Nutrition and Food Science University of Bonn Institute for Genomics Statistics and Bioinformatics University Hospital Bonn Central Laborator Clinical Study Core Unit Study Center Bonn Institute of Clinical Chemistry and Clinical Pharmacology University Hospital Bonn Department of Genomics Life & Brain Center University of Bonn Institute of Human Genetics University of Bonn Nutritional Physiology Institute of Nutrition and Food Science University of Bonn Bonn Germany

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Effects Of Dietary Intervention On Plasma Lipid Profile Is Linked To Changes In The Microbiome Composition

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Clinical Nutrition ESPEN 2023年 54卷 499-499页

作者： Schieren, A. Huber, H. Mantri, A. Seel, W. Stoffel-Wagner, B. Coenen, M. Nöthen, M. Schmid, M. Weinhold, L. Krawitz, P. Stehle, P. Simon, M.-C. Nutrition and Microbiota Nutritional Physiology University of Bonn Institute for Genomic Statistics and Bioinformatics Central Laboratory Clinical Study Core Unit Institute of Human Genetics Institute of Medical Biometry Informatics and Epidemiology University Hospital Bonn Bonn Germany

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

关键词： Large datasets

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Learning and teaching biological data science in the Bioconductor community

arXiv

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arXiv 2024年

作者： Drnevich, Jenny Tan, Frederick J. Almeida-Silva, Fabricio Castelo, Robert Culhane, Aedin C. Davis, Sean Doyle, Maria A. Geistlinger, Ludwig Ghazi, Andrew R. Holmes, Susan Lahti, Leo Mahmoud, Alexandru Nishida, Kozo Ramos, Marcel Rue-Albrecht, Kevin Shih, David J.H. Gatto, Laurent Soneson, Charlotte Roy J. Carver Biotechnology Center University of Illinois Urbana-Champaign IL United States Johns Hopkins University Department of Biology BaltimoreMD United States Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium VIB Center for Plant Systems Biology Ghent Belgium Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Spain Limerick Digital Cancer Research Centre School of Medicine University of Limerick Ireland University of Colorado Anschutz School of Medicine DenverCO United States Core for Computational Biomedicine Department of Biomedical Informatics Harvard Medical School BostonMA United States Statistics Department Stanford University StanfordCA United States Department of Computing University of Turku Finland Channing Division of Network Medicine Harvard Medical School BostonMA United States RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuoku Hyogo Kobe Japan Department of Epidemiology and Biostatistics City University of New York School of Public Health New YorkNY United States MRC WIMM Centre for Computational Biology MRC Weatherall Institute of Molecular Medicine University of Oxford Oxford United Kingdom School of Biomedical Sciences Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Computational Biology and Bioinformatics Unit de Duve Institute UCLouvain Brussels Belgium Friedrich Miescher Institute for Biomedical Research Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the *** Codes 97K80 © 2024, CC BY.

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Incorporating Participants’ Welfare into Sequential Multiple Assignment Randomized Trials

arXiv

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arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

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Making SMART decisions in prophylaxis and treatment studies

arXiv

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arXiv 2022年

作者： Mahar, Robert K. Lee, Katherine J. Chakraborty, Bibhas Salim, Agus Simpson, Julie A. Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne ParkvilleVIC Australia Clinical Epidemiology and Biostatistics Unit Murdoch Children’s Research Institute ParkvilleVIC Australia Department of Paediatrics Melbourne Medical School University of Melbourne ParkvilleVIC Australia Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Statistics Faculty of Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Mathematics and Statistics Faculty of Science University of Melbourne ParkvilleVIC Australia Baker Department of Cardiometabolic Health Melbourne Medical School Faculty of Medicine Dentistry and Health Sciences University of Melbourne ParkvilleVIC Australia

The optimal prophylaxis, and treatment if the prophylaxis fails, for a disease may be best evaluated using a sequential multiple assignment randomised trial (SMART). A SMART is a multi-stage study that randomises a participant to an initial treatment, observes some response to that treatment and then, depending on their observed response, randomises the same participant to an alternative treatment. Response adaptive randomisation may, in some settings, improve the trial participants’ outcomes and expedite trial conclusions, compared to fixed randomisation. But ‘myopic’ response adaptive randomisation strategies, blind to multistage dynamics, may also result in suboptimal treatment assignments. We propose a ‘dynamic’ response adaptive randomisation strategy based on Q-learning, an approximate dynamic programming algorithm. Q-learning uses stage-wise statistical models and backward induction to incorporate late-stage ‘payoffs’ (i.e. clinical outcomes) into early-stage ‘actions’ (i.e. treatments). Our real-world example consists of a COVID-19 prophylaxis and treatment SMART with qualitatively different binary endpoints at each stage. Standard Q-learning does not work with such data because it cannot be used for sequences of binary endpoints. Sequences of qualitatively distinct endpoints may also require different weightings to ensure that the design guides participants to regimens with the highest utility. We describe how a simple decision-theoretic extension to Q-learning can be used to handle sequential binary endpoints with distinct utilities. Using simulation we show that, under a set of binary utilities, the ‘dynamic’ approach increases expected participant utility compared to the fixed approach, sometimes markedly, for all model parameters, whereas the ‘myopic’ approach can actually decrease utility. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction

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Journal of Molecular Biology 2022年第11期434卷 167556-167556页

作者： Sanchez-Garcia, R. Macias, J.R. Sorzano, C.O.S. Carazo, J.M. Segura, J. Biocomputing Unit National Center for Biotechnology (CSIC) Darwin 3 Campus Univ. Autónoma de Madrid Cantoblanco Madrid 28049 Spain Oxford Protein Informatics Group Department of Statistics University of Oxford 29 St Giles' Oxford OX1 3LB UK United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://***. © 2022 The Authors

关键词： binding site machine learning protein interactions web server

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Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

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Journal of Allergy and Clinical Immunology: In Practice 2025年第6期13卷 1385-1396页

作者： Pinot de Moira, Angela Aurup, Anne V. Avraam, Demetris Zugna, Daniela Jensen, Aksel Karl Georg Welten, Marieke Cadman, Timothy de Lauzon-Guillain, Blandine Duijts, Liesbeth Elhakeem, Ahmed Esplugues, Ana Garcia-Aymerich, Judith García-Baquero, Gonzalo González Safont, Llúcia Harris, Jennifer R. Iñiguez, Carmen Jaddoe, Vincent W.V. McEachan, Rosemary R.C. Nader, Johanna L.T. Santa Marina, Loreto Swertz, Morris A. Tafflet, Muriel Vrijheid, Martine Wright, John Yang, Tiffany C. Taylor-Robinson, David Richiardi, Lorenzo Nybo Andersen, Anne-Marie School of Public Health Imperial College London London United Kingdom Department of Public Health University of Copenhagen Copenhagen Denmark Department of Nutrition Exercise and Sports University of Copenhagen Copenhagen Denmark Department of Public Health Policy and Systems University of Liverpool Liverpool United Kingdom Cancer Epidemiology Unit Department of Medical Sciences University of Turin Turin Italy Department of Pediatrics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Generation R Study Group Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Genomics Coordination Center University Medical Center Groningen University of Groningen Groningen Netherlands Department of Genetics University Medical Center Groningen University of Groningen Groningen Netherlands Université Paris Cité and Université Sorbonne Paris Nord INSERM INRAE Centre for Research in Epidemiology and Statistics Paris France Population Health Science Bristol Medical School Bristol United Kingdom MRC Integrative Epidemiology Unit at the University of Bristol Bristol United Kingdom Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP) Madrid Spain Department of Nursing University of Valencia Valencia Spain Epidemiology and Environmental Health Joint Research Unit the Foundation for the Promotion of Health and Biomedical Research of Valencia Region Universitat Jaume I-Universitat de València Valencia Spain Barcelona Institute for Global Health (ISGlobal) Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CEADIR Faculty of Biology University of Salamanca Campus Miguel de Unamuno Avda Licenciado Méndez Nieto s/n Salamanca Spain Biodonostia Environmental Epidemiology and Child Development Group San Sebastian Spain Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Statistics and Operational Research Universitat de València València Spain Bradford Institute f

Background: Children growing up in disadvantaged socioeconomic circumstances (SECs) have an increased risk of asthma. Objective: To increase our understanding of the pathways to inequalities in asthma and potential targets for intervention by (1) examining how the social patterning of asthma and its early-life risk factors varies across countries and (2) quantifying the mediation of observed inequalities by early-life risk factors. Methods: We used data for 107,884 mother-child dyads from 7 European birth cohorts across 6 countries. Maternal education was the primary exposure measure of early-life SECs. The outcome was current asthma in childhood (3-12 years). Inequalities were examined using multivariable regression and random effects meta-analysis. The mediating effects of early-life risk factors (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) were examined using counterfactual mediation analysis. Results: In meta-analysis, children of mothers with low/medium versus high education had a 17% (95% confidence interval: 8%-27%, I² = 21.6%) increased risk of asthma. Cohort-specific risk ratios ranged between 1.07 (0.97-1.18, Danish National Birth Cohort, Denmark) and 1.61 (1.08-2.40, study on the pre- & early postnatal determinants of child health & development, France). The early-life risk factors were similarly socially patterned, but with greater heterogeneity across cohorts (I² range = 66.2%-95.3%). The mediation analysis suggested that these factors play a relevant role in mediating observed inequalities (proportion mediated range: 0.08-0.72). Conclusions: There was a consistent tendency for children from disadvantaged SECs to be at greater risk of asthma in the European cohorts examined. Our results suggest that early-life risk factors partially mediate these disparities and, therefore, that public health interventions in the perinatal period may help to address inequalities in asthma. © 2025 The Autho

关键词： Birth cohort Childhood asthma Cross-cohort Inequalities Meta-analysis Socioeconomic

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