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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Basulo-Ribeiro, Juliana Matos, Nádia C. G. Araujo, Sabrina Magalhães Capela, Nuno Bischoff, Francisco Teixeira, Leonor Cruz-Correia, Ricardo University of Aveiro Aveiro Portugal Faculty of Medicine University of Porto Porto Portugal Faculty of Medicine of the University of Porto Health Data Science Ph.D. Program Porto Portugal CINTESIS - Center for Health Technology and Services Research Faculty of Medicine University of Porto Porto Portugal VirtualCare Portugal

With the advance of artificial intelligence and natural language processing technology, a new tool is standing out in the field of understanding and generating natural language in a sophisticated way: the Large Language Model (LLM). According to several authors, LLMs can be used for various types of medical cases, providing access to different sources of information, and have opened up countless opportunities in the healthcare sector. This work aims to share the lessons learned during the process of developing an LLM-based assistant aimed at specific pathologies that are more prevalent in the elderly in order to support caregivers, whether they are private individuals, home care organizations, nursing homes or others. This study has a significant potential impact on the community by providing access to detailed information on developing an LLM-based assistant. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

arXiv

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arXiv 2018年

作者： Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon Analytics and Data Science Ph.D. Program Kennesaw State University United States Analytics and Data Science Institute Kennesaw State University United States Ermas Consulting Inc. United States

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost benefit analysis

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Correction to: Multiplexed inhibition of immunosuppressive genes with Cas13d for combinatorial cancer immunotherapy (Nature Biotechnology, (2025), 10.1038/s41587-024-02535-2)

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Nature Biotechnology 2025年

作者： Zhang, Feifei Chow, Ryan d. He, Emily dong, Chuanpeng Xin, Shan Mirza, daniyal Feng, Yanzhi Tian, Xiaolong Verma, Nipun Majety, Medha Zhang, Yueqi Wang, Guangchuan Chen, Sidi Department of Genetics Yale University School of Medicine New HavenCT United States System Biology Institute Yale University West HavenCT United States Center for Cancer Systems Biology Yale University West HavenCT United States M.D.–Ph.D. Program Yale University West HavenCT United States Molecular Cell Biology Genetics and Development Program Yale University New HavenCT United States Yale College New HavenCT United States Immunobiology Program Yale University New HavenCT United States Department of Therapeutic Radiology Yale University School of Medicine New HavenCT United States Department of Neurosurgery Yale University School of Medicine New HavenCT United States Yale Comprehensive Cancer Center Yale University School of Medicine New HavenCT United States Yale Stem Cell Center Yale University School of Medicine New HavenCT United States Yale Center for Biomedical Data Science Yale University School of Medicine New HavenCT United States Key Laboratory of Multi-Cell Systems Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China

Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02535-2, published online 16 January 2025. In the version of the article initially published, Sidi Chen was incorrectly associated with a present address. This has now been corrected in the HTML and PdF versions of the article. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature Communications 2025年第1期16卷 1-21页

作者： Hefei Zhang Xuhang Li Shivani Nanda Albertha J. M. Walhout dongyuan Song Jingyi Jessica Li Onur Yukselen Alper Kucukural Manuel Garber Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA USA Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA USA Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA USA Via Scientific Inc. Cambridge MA USA Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA USA

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirdE. EmpirdE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify dEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

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discrete null field equation methods for solving Laplace's equation: Boundary layer computations

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Numerical Methods for Partial differential Equations 2024年第4期40卷 e23092-e23092页

作者： Zhang, Li-Ping Li, Zi-Cai Lee, Ming-Gong Huang, Hung-Tsai School of Mathematical Sciences Qufu Normal University Shandong Qufu China Department of Applied Mathematics National Sun Yat-sen University Kaohsiung Taiwan Department of Tourism and Leisure/Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Data Science and Analytics I-Shou University Kaohsiung Taiwan

Consider dirichlet problems of Laplace's equation in a bounded simply-connected domain (Formula presented.), and use the null field equation (NFE) of Green's representation formulation, where the source nodes (Formula presented.) are located on a pseudo-boundary (Formula presented.) outside (Formula presented.) but not close to its boundary (Formula presented.). Simple algorithms are proposed in this article by using the central rule for the NFE, and the normal derivatives (Formula presented.) of the solutions on the boundary (Formula presented.) can be easily obtained. These algorithms are called the discrete null field equation method (dNFEM) because the collocation equations are, indeed, the direct discrete form of the NFE. The bounds of the condition number are like those by the method of fundamental solutions (MFS) yielding the exponential growth as the number of unknowns increases. One trouble of the dNFEM is the near singularity of integrations for the solutions in boundary layers in Green's representation formulation. The traditional BEM also suffers from the same trouble. To deal with the near singularity, quadrature by expansions and the sinh transformation are often used. To handle this trouble, however, we develop two kinds of new techniques: (I) the interpolation techniques by Taylor's formulas with piecewise (Formula presented.) -degree polynomials and the Fourier series, and (II) the mini-rules of integrals, such as the mini-Simpson's and the mini-Gaussian rules. Error analysis is made for technique I to achieve optimal convergence rates. Numerical experiments are carried out for disk domains to support the theoretical analysis made. The numerical performance of the dNFEM is excellent for disk domains to compete with the MFS. The errors with (Formula presented.) can be obtained by combination algorithms, which are satisfactory for most engineering problems. In summary, the new simple dNFEM is based on the NFE, which is different from the boundary elem

关键词： Fourier series

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THE OVERSELLING OF GENETIC ANXIETY

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HASTINGS CENTER REPORT 1982年第5期12卷 20-27页

作者： GOOdMAN, MJ GOOdMAN, LE MadeleineJ. Goodman Ph.D.is Director Women's Studies Program and associate professor General Science and Women's Studies University of Hawaii. She is also principal data analyst of the Hawaii Breast Cancer Detection Project. LennE. Goodman D. Phil.is professor of philosophy University of Hawaii. His most recent book isMonotheism: A Philosophic Inquiry into the Foundations of Natural Theology and Ethics.

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Enriching Smart Cities by Optimizing Electric Vehicle Ride-Sharing through Game Theory

Enriching Smart Cities by Optimizing Electric Vehicle Ride-S...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： darko Radakovic Anuradha Singh Aparna S. Varde Pankaj Lal Environmental Science and Management Ph.D. Program Montclair State University NJ USA Clean Energy and Sustainability Analytics Center (CESAC) Montclair State University NJ USA Department of Computer Science Montclair State University NJ USA

Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging). Experimental results reveal that rising passenger numbers yield maximum battery savings (e.g. rush hours / major events); followed by stable and falling numbers. Our findings indicate that E-Ride-Minimax can reduce battery usage in some circumstances by 64%, losing 1 % profit. This is vital, given global emphasis on climate change. It increases cost-effectiveness, consumer participation and passengers per mile; reduces energy use and greenhouse gas emissions; and thus helps decarbonize transportation.

关键词： Climate change Smart cities Artificial intelligence Economics Smart devices Environmental factors Shared transport

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Categorical, low-dimensional decomposition of human odor space with non-negative matrix factorization

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Flavour 2014年第1期3卷 1-2页

作者： JB Castro SP Quinn A Ramanathan CS Chennubhotla Department of Psychology Bates College Lewiston USA Joint CMU-Pitt Ph.D. Program in Computational Biology University of Pittsburgh Pittsburgh USA Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge USA Department of Computational and Systems Biology University of Pittsburgh Pittsburgh USA

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An Introduction to Verification of Visualization Techniques

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2015年

作者： Tiago Etiene Robert M. Kirby Claudio T. Silva

ISBN: (数字)9781627058346

As we increase our reliance on computer-generated information, often using it as part of our decision-making process, we must devise tools to assess the correctness of that information. Consider, for example, software embedded on vehicles, used for simulating aircraft performance, or used in medical imaging. In those cases, software correctness is of paramount importance as there"s little room for error. Software verification is one of the tools available to attain such goals. Verification is a well known and widely studied subfield of computer science and computational science and the goal is to help us increase confidence in the software implementation by verifying that the software does what it is supposed to do. The goal of this book is to introduce the reader to software verification in the context of visualization. In the same way we became more dependent on commercial software, we have also increased our reliance on visualization software. The reason is simple: visualization is the lens through which users can understand complex data, and as such it must be verified. The explosion in our ability to amass data requires tools not only to store and analyze data, but also to visualize it. This book is comprised of six chapters. After an introduction to the goals of the book, we present a brief description of both worlds of visualization (Chapter 2) and verification (Chapter 3). We then proceed to illustrate the main steps of the verification pipeline for visualization algorithms. We focus on two classic volume visualization techniques, namely, Isosurface Extraction (Chapter 4) and direct Volume Rendering (Chapter 5). We explain how to verify implementations of those techniques and report the latest results in the field of verification of visualization techniques. The last chapter concludes the book and highlights new research topics for the future.

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Epileptic seizure detection

Epileptic seizure detection

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作者： Schuyler, Ronald White, Andrew Staley, Kevin Cios, Krzysztof J. Computational Bioscience Ph.D. Program UCDHSC United States University of Colorado United States American Epilepsy Society Society for Neuroscience United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Staley Lab. United States University of Colorado at Denver Health Sciences Center United States University of Colorado Bioenergetics Institute United States Data Mining and Bioinformatics Laboratory Turkey Polish Academy of Arts and Sciences Poland Dept. of Computer Science and Engineering University of Colorado at Denver Health Sciences Center P.O. Box 173364 Denver CO 80217-3364 United States

No abstract available

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