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Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

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Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

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Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

Forecasting Thailand’s Precipitation with Cascading Model o...

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International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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Dynamic searchable symmetric encryption schemes supporting range queries with forward/backward privacy

arXiv

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arXiv 2019年

作者： Zuo, Cong Sun, Shi-Feng Liu, Joseph K. Shao, Jun Pieprzyk, Josef Faculty of Information Technology Monash University Clayton3168 Australia Data61 CSIRO Melbourne/Sydney Australia School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou Zhejiang310018 China Institute of Computer Science Polish Academy of Sciences Warsaw01-248 Poland

Dynamic searchable symmetric encryption (DSSE) is a use-ful cryptographic tool in encrypted cloud storage. However, it has been reported that DSSE usually suffers from file-injection attacks and con-tent leak of deleted documents. To mitigate these attacks, forward pri-vacy and backward privacy have been proposed. Nevertheless, the ex-isting forward/backward-private DSSE schemes can only support single keyword queries. To address this problem, in this paper, we propose two DSSE schemes supporting range queries. One is forward-private and supports a large number of documents. The other can achieve backward privacy, while it can only support a limited number of documents. Fi-nally, we also give the security proofs of the proposed DSSE schemes in the random oracle model. Copyright © 2019, The Authors. All rights reserved.

关键词： Cryptography

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Inverse-dirichlet weighting enables reliable training of physics informed neural networks

arXiv

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arXiv 2021年

作者： Maddu, Suryanarayana Sturm, Dominik Müller, Christian L. Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden01069 Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden01307 Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Computational Mathematics Flatiron Institute New YorkNY United States Department of Statistics LMU München Munich Germany Institute of Computational Biology Helmholtz Zentrum München Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany Cluster of Excellence Physics of Life TU Dresden Germany Helmholtz-Zentrum Dresden-Rossendorf DresdenD-01328 Germany GörlitzD-02826 Germany

We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs). PINNs are popular machine-learning templates that allow for seamless integration of physical equation models with data. Their training amounts to solving an optimization problem over a weighted sum of data-fidelity and equation-fidelity objectives. Conflicts between objectives can arise from scale imbalances, heteroscedasticity in the data, stiffness of the physical equation, or from catastrophic interference during sequential training. We explain the training pathology arising from this and propose a simple yet effective inverse-Dirichlet weighting strategy to alleviate the issue. We compare with Sobolev training of neural networks, providing the baseline of analytically ǫ-optimal training. We demonstrate the effectiveness of inverse-Dirichlet weighting in various applications, including a multi-scale model of active turbulence, where we show orders of magnitude improvement in accuracy and convergence over conventional PINN training. For inverse modeling using sequential training, we find that inverse-Dirichlet weighting protects a PINN against catastrophic forgetting. © 2021, CC BY-SA.

关键词： Inverse problems

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Correction to: NoRCE: non‑coding RNA sets cis enrichment tool

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BMC bioinformatics 2021年第1期22卷 393页

作者： Gulden Olgun Afshan Nabi Oznur Tastan Department of Computer Engineering Bilkent University Ankara Turkey. Cancer Data Science Lab National Cancer Institute National Institute of Health Bethesda MD USA. Faculty of Engineering and Natural Sciences Sabanci University 34956 Istanbul Turkey. Faculty of Engineering and Natural Sciences Sabanci University 34956 Istanbul Turkey. otastan@sabanciuniv.edu.

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Exploring Uncertainty in Image Segmentation Ensembles

Exploring Uncertainty in Image Segmentation Ensembles

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20th Eurographics/IEEE VGTC Conference on Visualization, EuroVis 2018

作者： Fröhler, B. Möller, T. Weissenböck, J. Hege, H.-C. Kastner, J. Heinzl, C. Research Group Computed Tomography University of Applied Sciences Upper Austria Austria Visualization and Data Analysis Research Group Faculty of Computer Science University of Vienna Austria Visual Data Analysis Department Zuse Institute Berlin Germany

ISBN: (纸本)9783038680659

Finding the most accurate image segmentation involves analyzing results from different algorithms or parameterizations. In this work, we identify different types of uncertainty in this analysis that are represented by the results of probabilistic algorithms, by the local variability in the segmentation, and by the variability across the segmentation ensemble. We propose visualization techniques for the analysis of such types of uncertainties in segmentation ensembles. For a global analysis we provide overview visualizations in the image domain as well as in the label space. Our probability probing and scatter plot based techniques facilitate a local analysis. We evaluate our techniques using a case study on industrial computed tomography data. © 2018 The Author(s)

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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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Computing and Software for Big science 2024年第1期8卷 1-21页

作者： Allaire, C. Ammendola, R. Aschenauer, E.-C. Balandat, M. Battaglieri, M. Bernauer, J. Bondì, M. Branson, N. Britton, T. Butter, A. Chahrour, I. Chatagnon, P. Cisbani, E. Cline, E.W. Dash, S. Dean, C. Deconinck, W. Deshpande, A. Diefenthaler, M. Ent, R. Fanelli, C. Finger, M. Fol, E. Furletov, S. Gao, Y. Giroux, J. Waduge, N. C. Gunawardhana Hassan, O. Hegde, P.L. Hernández-Pinto, R.J. Blin, A. Hiller Horn, T. Huang, J. Jalotra, A. Jayakodige, D. Joo, B. Junaid, M. Kalantarians, N. Karande, P. Kriesten, B. Elayavalli, R. Kunnawalkam Li, Y. Lin, M. Liu, F. Liuti, S. Matousek, G. McEneaney, M. McSpadden, D. Menzo, T. Miceli, T. Mikuni, V. Montgomery, R. Nachman, B. Nair, R.R. Niestroy, J. Oregon, S. A. Ochoa Oleniacz, J. Osborn, J.D. Paudel, C. Pecar, C. Peng, C. Perdue, G.N. Phelps, W. Purschke, M.L. Rajendran, H. Rajput, K. Ren, Y. Renteria-Estrada, D.F. Richford, D. Roy, B.J. Roy, D. Saini, A. Sato, N. Satogata, T. Sborlini, G. Schram, M. Shih, D. Singh, J. Singh, R. Siodmok, A. Stevens, J. Stone, P. Suarez, L. Suresh, K. Tawfik, A.-N. Acosta, F. Torales Tran, N. Trotta, R. Twagirayezu, F.J. Tyson, R. Volkova, S. Vossen, A. Walter, E. Whiteson, D. Williams, M. Wu, S. Zachariou, N. Zurita, P. Université Paris-Saclay CNRS/IN2P3 IJCLAB Orsay 91400 France Sezione di Roma Tor Vergata INFN Rome 00133 Italy Brookhaven National Lab Upton 11973 NY United States Meta Menlo Park 935025 CA United States National Institute for Nuclear Physics Genoa 16146 Italy Center for Frontiers in Nuclear Science Stony Brook University Stony Brook 11790-3800 NY United States Stony Brook University Stony Brook 11794 NY United States National Institute for Nuclear Physics Catania 95125 Italy Drexel University Philadelphia 19104 PA United States Messiah University Mechanicsburg 17055 PA United States Jefferson Lab Newport News 293606 VA United States Laboratory Nuclear and High-Energy Physics CNRS Paris 75005 France University of Michigan Ann Arbor 48104 MI United States National Institute for Nuclear Physics Roma Rome 00185 Italy Indian Institute of Technology Bombay Maharashtra Mumbai 400076 India Massachusetts Institute of Technology Cambridge 021339 MA United States Department of Physics University of Manitoba Winnipeg R3T 2N2 MB Canada William & amp Charles University Faculty of Mathematics and Physics Prague 18000 Czech Republic Accelerators and Beam Physics Group CERN Geneva 1211 Switzerland University of Regina Regina S4S0A2 SK Canada University of Virginia Charlottesville 22904 VA United States University of Victoria Victoria V8P 5C2 Canada Central University of Karnataka Kalaburagi 585367 India Facultad de Ciencias Físico-Matemáticas Universidad Autónoma de Sinaloa Ciudad Universitaria Sinaloa Culiacán 80000 Mexico Institute for Theoretical Physics TübingenUniversity Tübingen 72076 Germany The Catholic University of America Washington 20064 DC United States University of Jammu Jammu and Kashmir Jammu 180006 India Hampton University Hampton 23668 VA United States Oak Ridge National Laboratory Oak Ridge 37830 TN United States Virginia Union University Richmond 23220 VA United States Lawrence Livermore N

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. © The Author(s) 2024.

关键词： Artificial Intelligence Deep learning EIC ePIC Machine learning Physics QCD

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UNRAVELING RADIOMICS COMPLEXITY: STRATEGIES FOR OPTIMAL SIMPLICITY IN PREDICTIVE MODELING

arXiv

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arXiv 2024年

作者： Loutfi, Mahdi Ait Lhaj Podasca, Teodora Boblea Zwanenburg, Alex Upadhaya, Taman Ginart, Jorge Barrios Raleigh, David R. Chen, William C. Capaldi, Dante P.I. Zheng, Hong Gevaert, Olivier Wu, Jing Silva, Alvin C. Zhang, Paul J. Bai, Harrison X. Seuntjens, Jan Löck, Steffen Richard, Patrick O. Morin, Olivier Reinhold, Caroline Lepage, Martin Vallières, Martin Department of Computer Science Université de Sherbrooke SherbrookeQC Canada Department of Surgery Service of Urology Université de Sherbrooke SherbrookeQC Canada OncoRay National Center for Radiation Research in Oncology Faculty of Medicine University Hospital Carl Gustav Carus TUD Dresden University of Technology Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany Germany Heidelberg Germany Faculty of Medicine University Hospital Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Dresden Germany Department of Radiation Oncology Cedars-Sinai Medical Center Los AngelesCA United States Department of Radiation Oncology University of California San Francisco San FranciscoCA United States Departments of Radiation Oncology Neurological Surgery and Pathology University of California San Francisco San FranciscoCA United States Center for Biomedical Informatics Research School of Medicine Stanford University CA94305 United States Department of Medicine Department of Biomedical Data Science Stanford University StanfordCA94305 United States Department of Radiology The Second Xiangya Hospital of Central South University Hunan Changsha410011 China Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd. ScottsdaleAZ85259 United States Department of Pathology and Clinical Medicine Hospital of the University of Pennsylvania PhiladelphiaPA United States Department of Radiology and Radiological Sciences Johns Hopkins BaltimoreMD United States Princess Margaret Cancer Centre University Health Network Departments of Radiation Oncology & Medical Biophysics University of Toronto TorontoON Canada Division of Urology Centre Hospitalier Universitaire de Sherbrooke Université de Sherbrooke Cancer Research Institute SherbrookeQC Canada The Research Institute The McGill University Health Center MontrealQC Canada Département de Médecine Nucléaire et Radiobiologie Université de Sherbrooke SherbrookeQC Canada Centre d’imager

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: To establish a methodology and provide tools for identifying and explaining the smallest set of predictive features radiomic features. Materials and Methods: Radiomic features (a total of 89,714) were extracted from five distinct datasets with different cancer types: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). These features were categorized into complexity levels, defined by the number of computational steps required for their computation, encompassing morphological, intensity, texture, linear filters-based, and nonlinear filter-based features. For every dataset, models were trained on each complexity level specifically to classify clinical outcomes, and their performance was evaluated using the area under the curve (AUC). The most informative features were identified and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was designed and implemented to streamline radiomic studies through both code-based and graphical-based approaches, and was applied using our proposed methodology to analyze the datasets. Morphological features were found to be optimal in two cases: for MRI-based meningioma (AUC: 0.65;sensitivity: 64%;specificity: 62%;95% CI: 0.59, 0.72) and MRI-based low-grade glioma (AUC: 0.68;sensitivity: 68%;specificity: 69%;95% CI: 0.60, 0.75). Additionally, intensity features were optimal in two instances: for contras

关键词： Classification (of information)

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From modular decomposition trees to rooted median graphs

arXiv

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arXiv 2021年

作者： Bruckmann, Carmen Stadler, Peter F. Hellmuth, Marc Helmholtz-Centre for Environmental Research Permoserstraße 15 LeipzigD-04318 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden

The modular decomposition of a symmetric map δ: X ×X → ϒ (or, equivalently, a set of symmetric binary relations, a 2-structure, or an edge-colored undirected graph) is a natural construction to capture key features of δ in labeled trees. A map δ is explained by a vertex-labeled rooted tree (T,t) if the label δ(x,y) coincides with the label of the last common ancestor of x and y in T, i.e., if δ(x,y) = t(lca(x,y)). Only maps whose modular decomposition does not contain prime nodes, i.e., the symbolic ultrametrics, can be exaplained in this manner. Here we consider rooted median graphs as a generalization to (modular decomposition) trees to explain symmetric maps. We first show that every symmetric map can be explained by "extended" hypercubes and half-grids. We then derive a a linear-time algorithm that stepwisely resolves prime vertices in the modular decomposition tree to obtain a rooted and labeled median graph that explains a given symmetric map δ. We argue that the resulting "tree-like" median graphs may be of use in phylogenetics as a model of evolutionary relationships. © 2021, CC BY.

关键词： Undirected graphs

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Tailored Fluorosurfactants through Controlled/Living Radical Polymerization for Highly Stable Microfluidic Droplet Generation

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Angewandte Chemie 2023年第3期136卷

作者： Xiangke Li Shi-Yang Tang Yang Zhang Jiayuan Zhu Helen Forgham Chun-Xia Zhao Cheng Zhang Thomas P. Davis Ruirui Qiao Australian Institute of Bioengineering and Nanotechnology The University of Queensland Brisbane Queensland 4072 Australia Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK School of Engineering Faculty of Science and Engineering Macquarie University Sydney NSW 2109 Australia School of Chemical Engineering and Advanced Materials The University of Adelaide Adelaide SA 5005 Australia Contribution: Formal analysis (equal) Investigation (equal) Supervision (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Funding acquisition (equal) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Funding acquisition (lead) Methodology (lead) Supervision (lead) Writing - original draft (equal) Writing - review & editing (lead)

Droplet-based microfluidics represents a disruptive technology in the field of chemistry and biology through the generation and manipulation of sub-microlitre droplets. To avoid droplet coalescence, fluoropolymer-based surfactants are commonly used to reduce the interfacial tension between two immiscible phases to stabilize droplet interfaces. However, the conventional preparation of fluorosurfactants involves multiple steps of conjugation reactions between fluorinated and hydrophilic segments to form multiple-block copolymers. In addition, synthesis of customized surfactants with tailored properties is challenging due to the complex synthesis process. Here, we report a highly efficient synthetic method that utilizes living radical polymerization (LRP) to produce fluorosurfactants with tailored functionalities. Compared to the commercialized surfactant, our surfactants outperform in thermal cycling for polymerase chain reaction (PCR) testing, and exhibit exceptional biocompatibility for cell and yeast culturing in a double-emulsion system. This breakthrough synthetic approach has the potential to revolutionize the field of droplet-based microfluidics by enabling the development of novel designs that generate droplets with superior stability and functionality for a wide range of applications.

关键词： Droplet Microfluidics Fluorosurfactant Perfluoropolyether Living Radical Polymerization

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