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arXiv 2022年

作者： Bergmeir, Christoph de Nijs, Frits Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Genov, Evgenii Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui Department of Data Science and Artificial Intelligence Monash University Melbourne Australia School of Mathematics and Physics University of Queensland Brisbane Australia Centre for Energy Data Innovation School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia School of Electrical and Electronics Engineering University of Adelaide Adelaide Australia Honda Research Institute Europe GmbH Offenbach am Main63073 Germany School of Information Technology Deakin University Melbourne Australia Building and Property Division Monash University Melbourne Australia EVERGi MOBI Vrije Universiteit Brussel Brussels Belgium School of Economics and Management Beihang University Beijing China E.T.S. Ingenieros Informáticos Universidad Politécnica de Madrid Madrid28660 Spain Disciple of Business Analytics University of Sydney Australia IDLab Department of Information Technology Ghent University - imec Belgium Department of Computer Science Centro de Investigación en Matem áticas Monterrey66629 Mexico Mines Paris PSL University Sophia Antipolis06904 France DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the "IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年第12期124卷 1930-1943页

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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A Web-based Visual analytics Application for Biological Networks

A Web-based Visual Analytics Application for Biological Netw...

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22nd Eurographics / IEEE VGTC Conference on Visualization, EuroVis 2020

作者： Krone, M. Dräger, A. Cobanoglu, E. Harke, M.O. Hoene, M. Weigert, C. Lehmann, R. Big Data Visual Analytics in Life Sciences Department of Computer Science University of Tübingen Germany University of Tübingen Germany Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens Department of Computer Science University of Tübingen Germany Partner Site Tübingen Germany Institute for Clinical Chemistry and Pathobiochemistry University of Tübingen Germany Institute for Diabetes Research and Metabolic Diseases The Helmholtz Zentrum München The University of Tübingen Germany Oberschleissheim Germany

ISBN: (纸本)9783038681052

Modern high-throughput methods enable rapidly obtaining transcriptomics data, which includes information about the expression rate of genes. The expression rates are usually given as fold change, which describes the over- or under-expression of each gene. Each gene can be part of one or more biological pathways. A pathway models the interactions between molecules in an organism that lead to a particular chemical change. Consequently, many applications in medical research need to analyze the impact of gene expression changes on the biological pathways of an organism. It allows concluding diseases or other conditions of the organism. We present a web-based visual analytics application that facilitates exploring the network of biological pathways corresponding to a given set of genes. The network is constructed from pathways derived from an external database. Users can interactively zoom and filter the network and get details on demand. Our application is currently work in progress and is developed in close collaboration with medical researchers. In subsequent steps, we strive to add more features, such as the ability to compare data from different individuals or to visualize time series data. Furthermore, we want to extend our application to visualize not just transcriptomics but multi-omics data. © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association.

关键词： Gene expression

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Completely self-supervised crowd counting via distribution matching

arXiv

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arXiv 2020年

作者： Sam, Deepak Babu Agarwalla, Abhinav Joseph, Jimmy Sindagi, Vishwanath A. Babu, R. Venkatesh Patel, Vishal M. Video Analytics Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India Vision & Image Understanding Lab Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States

Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to map these features to the end task of density estimation. We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a large set of unlabeled crowd images, is the approximate upper limit of the crowd count for the given dataset. Our method dwells on the idea that natural crowds follow a power law distribution, which could be leveraged to yield error signals for backpropagation. A density regressor is first pretrained with self-supervision and then the distribution of predictions is matched to the prior by optimizing Sinkhorn distance between the two. Experiments show that this results in effective learning of crowd features and delivers significant counting performance. Furthermore, we establish the superiority of our method in less data setting as well. The code and models for our approach is available at https://***/val-iisc/css-ccnn. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Improving Software Engineering in Biostatistics: Challenges and Opportunities

arXiv

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arXiv 2023年

作者： Bové, Daniel Sabanés Seibold, Heidi Boulesteix, Anne-Laure Manitz, Juliane Gasparini, Alessandro Günhan, Burak K. Boix, Oliver Schüler, Armin Fillinger, Sven Nahnsen, Sven Jacob, Anna E. Jaki, Thomas Hoffmann-La Roche Ltd. Product Development Data Sciences Grenzacherstrasse 124 Basel4070 Switzerland IGDORE Elsenheimerstr. 48 München80687 Germany Institute for Medical Information Processing Biometry and Epidemiology LMU Munich Munich81377 Germany EMD Serono 45A Middlesex Turnpike BillericaMA01821 United States Department of Medical Epidemiology and Biostatistics Karolinska Institutet PO Box 281 Stockholm17177 Sweden Red Door Analytics AB Dianavägen 11A Stockholm11542 Sweden Merck Healthcare KGaA Frankfurter Strasse 250 Darmstadt64293 Germany Bayer AG Aprather Weg 18a Wuppertal42113 Germany University of Tübingen Tübingen72076 Germany Faculty of Informatics and Data Science University of Regensburg Bajuwarenstraße 4 Regensburg93053 Germany MRC Biostatistics Unit University of Cambridge East Forvie Building Forvie Site Robinson Way CambridgeCB2 0SR United Kingdom Biomedical Data Science Department of Computer Science University of Tübingen Tübingen72076 Germany

Programming is ubiquitous in applied biostatistics;adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in biostatistics. Silos between different statistician roles, projects, departments, and organizations lead to the development of duplicate and suboptimal code. Building on top of open-source software requires critical appraisal and risk-based assessment of the used modules. Code that is written needs to be readable to ensure reliable software. The software needs to be easily understandable for the user, as well as developed within testing frameworks to ensure that long term maintenance of the software is feasible. Finally, the reproducibility of research results is hindered by manual analysis workflows and uncontrolled code development. We next describe how the awareness of the importance and application of good software engineering practices and strategies can help address these challenges. The foundation is a better education in basic software engineering skills in schools, universities, and during the work life. Dedicated software engineering teams within academic institutions and companies can be a key factor for the establishment of good software engineering practices and catalyze improvements across research projects. Providing attractive career paths is important for the retainment of talents. Readily available tools can improve the reproducibility of statistical analyses and their use can be exercised in community events. Similarly, tools exist to assess the risk of R packages, and initiatives are developing shared repositories of trusted R packages. Finally, collaboration between software developers from different organizations is key to harness open-source software efficiently and optimally, while building trusted solutions. We illustrate the potential with examples of successful projects. Copyright © 2023, The Authors. All right

关键词： Open source software

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Structured graph learning via laplacian spectral constraints 33

Structured graph learning via laplacian spectral constraints

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33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

作者： Kumar, Sandeep Ying, Jiaxi de Cardoso, José Vinícius M. Palomar, Daniel P. Department of Industrial Engineering and Data Analytics Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observed samples is an NP-hard combinatorial problem. In this paper, we first show that for a set of important graph families it is possible to convert the structural constraints of structure into eigenvalue constraints of the graph Laplacian matrix. Then we introduce a unified graph learning framework, lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling that is capable of learning structures of a large class of graph families. The proposed algorithms are provably convergent and practically amenable for large-scale semi-supervised and unsupervised graph based learning tasks. Extensive numerical experiments with both synthetic and real data sets demonstrate the effectiveness of the proposed methods. © 2019 Neural information processing systems foundation. All rights reserved.

关键词： Laplace transforms

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"

arXiv

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arXiv 2022年

作者： Frizzarin, Maria Visentin, Giulio Ferragina, Alessandro Hayes, Elena Bevilacqua, Antonio Dhariyal, Bhaskar Domijan, Katarina Khan, Hussain Ifrim, Georgiana Le Nguyen, Thach Meagher, Joe Menchetti, Laura Singh, Ashish Whoriskey, Suzy Williamson, Robert Zappaterra, Martina Casa, Alessandro Teagasc Animal & Grassland Research and Innovation Centre Moorepark Ireland School of Mathematics and Statistics University College Dublin Ireland Department of Veterinary Medical Sciences University of Bologna Italy Teagasc Food Research Centre Ashtown Ireland Teagasc Food Research Centre Moorepark Ireland School of Computer Science University College Dublin Ireland Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Insight Centre for Data Analytics University College Dublin Ireland School of Biosciences and Veterinary Medicine University of Camerino Italy School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Department of Agricultural and Food Sciences University of Bologna Italy Faculty of Economics and Management Free University of Bozen-Bolzano Italy

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model. © 2022, CC BY.

关键词： Machine learning

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Entanglement-Induced Barren Plateaus

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PRX Quantum 2021年第4期2卷 040316-040316页

作者： Carlos Ortiz Marrero Mária Kieferová Nathan Wiebe Data Sciences and Analytics Group Pacific Northwest National Laboratory Richland Washington 99354 USA Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney New South Wales 2007 Australia Department of Computer Science University of Toronto Ontario M5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory Richland Washington 99354 USA

We argue that an excess in entanglement between the visible and hidden units in a quantum neural network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume law in the entanglement entropy will give rise to models that are not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden subsystem with high probability. We show how this can cause both gradient-descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden and/or visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus.

关键词： Machine learning Quantum algorithms

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C-Net: A reliable convolutional neural network for biomedical image classification

arXiv

引用

arXiv 2020年

作者： Barzekar, Hosein Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided diagnosis

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