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On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series

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arXiv 2023年

作者： Kuznetsova, Rita Pace, Alizée Burger, Manuel Yèche, Hugo Rätsch, Gunnar Department of Computer Science ETH Zürich Switzerland ETH AI Center ETH Zürich Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany Medical Informatics Unit Zürich University Hospital Zürich Switzerland Swiss Institute of Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland

Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods’ impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Hybrid classical-quantum method for diabetic foot ulcer classification

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arXiv 2021年

作者： Alavi, Azadeh Akhoundi, Hossein AI Discipline School of Computing Technologies RMIT University VIC3001 Australia Bioinformatics lab Baker Heart and Diabetes Institute VIC3002 Australia Security Business Unit Cisco Melbourne Australia

Diabetes is a raising problem that affects many people globally. Diabetic patients are at risk of developing foot ulcer that usually leads to limb amputation, causing significant morbidity, and psychological distress. In order to develop a self monitoring mobile application, it is necessary to be able to classify such ulcers into either of the following classes: Infection, Ischaemia, None, or Both. In this work, we compare the performance of a classical transfer-learning-based method, with the performance of a hybrid classical-quantum Classifier on diabetic foot ulcer classification task. As such, we merge the pre-trained Xception network with a multi-class variational classifier. Thus, after modifying and retraining the Xception network, we extract the output of a mid-layer and employ it as deep-features presenters of the given images. Finally, we use those deep-features to train multi-class variational classifier, where each classifier is implemented on an individual variational circuit. The method is then evaluated on the blind test set DFUC2021. The results proves that our proposed hybrid classical-quantum Classifier leads to considerable improvement compared to solely relying on transfer learning concept through training the modified version of Xception network. © 2021, CC BY-NC-SA.

关键词： Diseases

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale ai 41

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Position: Bayesian Deep Learning is Needed in the Age of Lar...

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

Pattern change of precipitation extremes in Bear Island

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arXiv 2023年

作者： Ray, Arnob Chakraborty, Tanujit Radhakrishnan, Athulya Hens, Chittaranjan Dana, Syamal K. Ghosh, Dibakar Murukesh, Nuncio Physics and Applied Mathematics Unit Indian Statistical Institute Kolkata700108 India Roux Institute Northeastern University Portland04101 United States Institute for Experiential AI Northeastern University Boston02115 United States Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris75005 France Ocean Atmospheric Studies National Centre for Antarctic and Ocean Research Vasco Da Gama403804 India Center for Computational Natural Science and Bioinformatics International Institute of Informational Technology Hyderabad500032 India Division of Dynamics Lodz University of Technology postcode Lodz90-924 Poland

Extreme precipitation in the Arctic region plays a crucial role in global weather and climate patterns. Bear Island (Bjørnøya) is located in the Norwegian Svalbard archipelago, which is, therefore, selected for our study on extreme precipitation. The island occupies a unique geographic position at the intersection of the high and low Arctic, characterized by a flat and lake-filled northern region contrasting with mountainous terrain along its southern shores. Its maritime-polar climate is influenced by North Atlantic currents, resulting in relatively mild winter temperatures. An increase in precipitation level in Bear Island is a significant concern linked to climate change and has global implications. We have collected the amount of daily precipitation as well as daily maximum temperatures from the meteorological station of Bjørnøya located on the island, operated by the Norwegian Centre for Climate Services in Svalbard for a period spanning from January 1, 1960 to December 31, 2021. We observe that the trend of yearly mean precipitation during this period linearly increases. Also, the number of extreme precipitation events is also increasing. Motivating from this observation, we analyze the recorded data to investigate the changing pattern of precipitation extremes over the climate scales. We employ the generalized extreme value distribution to model yearly and seasonal maxima of daily precipitation amount and determine the return levels and return period of precipitation extremes. We compare the variability of precipitation extremes between the two time periods: (i) 1960-1990 and (ii) 1991-2021. Our analysis reveals an increase in the frequency of precipitation extremes occurrences between 1991 and 2021. A relationship between temperature and precipitation in the area is discerned using wavelet transform coherence. Our findings establish a better understanding of precipitation extremes in Bear Island from a statistical viewpoint, with an observation of seasonal a

关键词： Wavelet transforms

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

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arXiv 2025年

作者： Li, Wenran Cadet, Xavier F. Medina-Ortiz, David Davari, Mehdi D. Sowdhamini, Ramanathan Damour, Cedric Li, Yu Miranville, Alain Cadet, Frederic University of Paris City University of Reunion INSERM BIGR DSIMB ParisF-75015 France Department of Computing Imperial College London United Kingdom Department of Computer Engineering University of Magallanes Punta Arenas Chile Centre for Biotechnology and Bioengineering Universidad de Chile Santiago Chile Department of Bioorganic Chemistry Leibniz Institute of Plant Biochemistry Halle06120 Germany National Centre for Biological Science TIFR Bangalore India Molecular Biophysics Unit Indian Institute of Science Bangalore India Institute of Bioinformatics and Applied Biotechnology Bangalore India EnergyLab EA 4079 Faculty of Sciences and Technology University of Reunion France School of Information Science and Technology Beijing Institute of Artificial Intelligence China Laboratoire de Mathématiques Appliquées University Le Havre Normandie le Havre France PEACCEL Artificial Intelligence Department AI for Bi-ologics Paris75013 France

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models (DDPM) and Score-based Generative Models (SGM), where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance in the E(3) group can maintain the physical stability of the N − Cα − C frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation. © 2025, CC BY.

关键词： Biomolecules

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

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

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

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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

111P Leveraging circulating tumor DNA sequencing for first-line cancer treatment: Insights from two prospective precision medicine studies

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Annals of Oncology 2024年 35卷 S258-S259页

作者： V. Debien S. Taleb S. Cousin L. Belcaid A. Bayle I. Taleb A. Besson N. El Ghazzi A. Moreau Y. Laizet M. Alamé D. Vasseur L. Blouin E. Khalifa L. Lacroix I. Soubeyran E. Rouleau C.P. Massard A. Italiano Early Phase Clinical Trials Unit Institute Bergonié - Centre Régional de Lutte Contre le Cancer (CLCC) Bordeaux France DITEP Institut Gustave Roussy Villejuif France Early Phase Trials Department Institut Bergonié Bordeaux France Department of Medical Oncology Copenhagen University Copenhagen Denmark Biostatistics and Epidemiology U1018 Oncostat Institut Gustave Roussy Villejuif France Radiotherapy Department Institut Bergonie Bordeaux France Department of Bioinformatics Institut Bergonié Bordeaux France Bioinformatics and AI Unit Institute Bergonié - Centre Régional de Lutte Contre le Cancer (CLCC) Bordeaux France Molecular Pathology Department Institute Bergonié - Centre Régional de Lutte Contre le Cancer (CLCC) Bordeaux France Medical Biology and Pathology Department Gustave Roussy - Cancer Campus Villejuif France Biopathology Department Institute Bergonié Bordeaux France Tumor Genetics Department Gustave Roussy - Cancer Campus Villejuif France Drug Development Department (DITEP) Gustave Roussy - Cancer Campus Villejuif France

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop