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4th International Conference on Information Management and Technology, ICIMTech 2019

作者： Maryani Mahesworo, Bharuno Perbangsa, Anzaludin Samsinga Hendarti, Henny Information Systems Department Bina Nusantara University Jakarta11480 Indonesia BioInformatics Data Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Currently, the society is very critical and demands valid information transparency. Information transparency is needed to maintain good governance and can also prevent corruption. However, information on the sustainability of development programs carried out by the government is much unknown to the public. In addition, the government must be burdened with public dissatisfaction due to fake news (hoax). The Ministry of Public Works has tried to build a website to disseminate its programs. However, measurement of the quality of the website is needed. This study uses questionnaires with a Likert scale to obtain user interface assessment data in accordance with the WebQual 4.0 method. This study shows that the easiness of the website to use has the highest correlation with how helpful the website is. Public works ministry websites need to more frequently update valid information and promote transparency. The society also wants online complaint platform to make it easier to solve problems. © 2019 IEEE.

关键词： User interfaces

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Cavity approach for the approximation of spectral density of graphs with heterogeneous structures

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Physical Review E 2024年第3期109卷 034303-034303页

作者： Grover E. C. Guzman Peter F. Stadler Andre Fujita Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics School of Excellence in Embedded Composite AI Dresden/Leipzig (SECAI) Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Humboldtstraße 25 04105 Leipzig Germany Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá 111321 Colombia and The Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil and Division of Network AI Statistics Medical Institute of Bioregulation Kyushu University Fukuoka 812-8582 Japan

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|hmax3t) and O(|E|hmax2t), respectively, where |E| is the number of edges, hmax is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

关键词： Network structure Directed networks Monte Carlo methods Network Models Topological tools

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Assessing the potential of PlanetScope satellite imagery to estimate particulate matter oxidative potential

arXiv

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arXiv 2024年

作者： Hough, Ian Argentier, Loïc Jiang, Ziyang Zheng, Tongshu Bergin, Mike Carlson, David Jaffrezo, Jean-Luc Chanussot, Jocelyn Uzu, Gaëlle Univ. Grenoble Alpes CNRS INRAE IRD Grenoble INP IGE Grenoble38000 France Data Science Experts Grenoble38000 France Department of Civil and Environmental Engineering Duke University DurhamNC27708 United States Duke Kunshan University Division of Natural and Applied Sciences Kunshan215316 China Duke Global Health Institute Duke University DurhamNC27708 United States Department of Biostatistics and Bioinformatics Duke University DurhamNC27708 United States Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Since OP is not routinely monitored, it can be challenging to estimate exposure and health impacts. Remote sensing data are commonly used to estimate PM mass concentration, but have never been used to estimate OP. In this study, we evaluate the potential of satellite images to estimate OP as measured by acellular ascorbic acid (OPAA) and dithiothreitol (OPDTT) assays of 24-hour PM10 sampled periodically over five years at three locations around Grenoble, France. We use a deep convolutional neural network to extract features of daily 3 m/pixel PlanetScope satellite images and train a multilayer perceptron to estimate OP at a 1 km spatial resolution based on the image features and common meteorological variables. The model captures more than half of the variation in OPAA and almost half of the variation in OPDTT (test set R2 = 0.62 and 0.48, respectively), with relative mean absolute error (MAE) of about 32%. Using only satellite images, the model still captures about half of the variation in OPAA and one third of the variation in OPDTT (test set R2 = 0.49 and 0.36, respectively) with relative MAE of about 37%. If confirmed in other areas, our approach could represent a low-cost method for expanding the temporal or spatial coverage of OP estimates. © 2024, CC BY.

关键词： Satellite imagery

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AB1448 DIFFERENTIATION OF IMMUNE MEDIATED VERSUS NON IMMUNE MEDIATED RHEUMATIC DISEASES BY ONLINE SYMPTOM CHECKER IN REAL-WORLD PATIENTS – MULTIPLE DIAGNOSES AND PARTICULARLY FIBROMYALGIA IS A STUMBLING BLOCK

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Annals of the Rheumatic Diseases 2024年 83卷 2082-2083页

作者： L. Qin F. Zegers D. Selani E.B. Van den Akker R. Bos S. Le Cessie I. Gehring H.K. Glas M. Johannesson J. Knitza L. Mathsson-Alm M.J.T. Reinders E. Steyerberg A. Van Tubergen L.M. Verhoef B. Axnäs L. Klareskog R. Knevel Leiden University Medical Center Department of Rheumatology Leiden Netherlands Leiden University Medical Center Department of Biomedical Data Sciences Leiden Netherlands Delft University of Technology Department of Intelligent Systems Pattern Recognition and Bioinformatics Delft Netherlands Leiden University Medical Center Leiden Computational Biology Department of Biomedical Data Sciences Leiden Netherlands Delft University of Technology The Delft Bioinformatics Lab Pattern Recognition and Bioinformatics Delft Netherlands Medical Center Leeuwarden Department of Rheumatology Leeuwarden Netherlands Nij Smellinghe Hospital Department of Rheumatology Drachten Netherlands Leiden University Medical Center Department of Clinical Epidemiology Leiden Netherlands Thermo Fisher Scientific Freiburg Germany Reumazorg Zuid West Nederland Goes Netherlands Karolinska Institutet and Karolinska University Hospital Department of Medicine Solna Solna Sweden University Hospital of Giessen and Marburg Philipps-University Marburg Institute for Digital Medicine Marburg Germany Uppsala University Department of Immunology Genetics and Pathology Uppsala Sweden Thermo Fisher Scientific Uppsala Sweden University Medical Center Rotterdam Department of Public Health Rotterdam Netherlands Maastricht University Medical Center Department of Internal Medicine Division of Rheumatology Maastricht Netherlands Maastricht University Care and Public Health Research Institute (CAPHRI) Maastricht Netherlands Sint Maartenskliniek Research Department Ubbergen Netherlands Elsa Science Stockholm Sweden Karolinska Institutet and Karolinska University hospital Department of Medicine Solna Solna Sweden Newcastle University Translational and Clinical Research Institute Rheumatology Newcastle upon Tyne United Kingdom

Background: Rheumatic? is an online symptom checker developed collaboratively by designers, engineers, clinical experts and patients. It is designed to identify individuals at high risk of developing rheumatic diseases and patients with immune-mediated rheumatic diseases (IRD). After completion of the questionnaire, Rheumatic? gives a weighted score based on expert assessments of the clinical significance of the answers [1] . A previous retrospective study has demonstrated the potential to distinguish IRD from osteoarthritis(OA) and gout, where clinicians filled out the questionnaire based on the medical notes [2] . In order to use a questionnaire in real life, it should be tested on real life participants, as they may describe their symptoms differently than experts. In order to evaluate the performance of the current Rheumatic? score, to differentiate between IRDs and non-IRDs we conducted a prospective real-world study in the Netherlands and assessed the differences in Rheumatic? total scores between individuals diagnosed with IRD and non-IRD. Objectives: Examine the difference in scores of Rheumatic? between IRD (Rheumatoid arthritis, Psoriatic arthritis, Myositis, Sjogren’s syndrome, SLE, Spondyloarthritis and Polymyalgia rheumatica) and non-IRD(OA, Gout, Fibromyalgia and other) diagnoses. Methods: We analyzed data submitted by Dutch participants recruited via multiple channels between July 2021 and July 2022. After completing the online questionnaire Rheumatic? participants received four follow-up surveys at 1 week, 3, 6 and 12 months asking, among other things, for previous and novel diagnoses. The theoretical total score for the participant was determined using the current, expert developed, risk assessment model post collection of all data [1,2] . We used t-test and analysis of variance (ANOVA) to compared the observed differences in mean and median of total scores among these groups. Results: 8,727 participants were included, of which 6861(79%) were female

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Addendum: Thousands of human non-AUG extended proteoforms lack evidence of evolutionary selection among mammals

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Nature communications 2024年第1期15卷 228-228页

作者： Fedorova, Alla D. Kiniry, Stephen J. Andreev, Dmitry E. Mudge, Jonathan M. Baranov, Pavel V. School of Biochemistry and Cell Biology University College Cork Cork Ireland SFI Centre for Research Training in Genomics Data Science University College Cork Cork Ireland School of Biochemistry and Cell Biology University College Cork Cork Ireland Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS Moscow Russian Federation Belozersky Institute of Physico-Chemical Biology Lomonosov Moscow State University Moscow Russian Federation European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge United Kingdom

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Distributed Analytics on Sensitive Medical data:The Personal Health Train

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data Intelligence 2020年第1期2卷 96-107,305,306,307页

作者： Oya Beyan Ananya Choudhury Johan van Soest Oliver Kohlbacher Lukas Zimmermann Holger Stenzhorn Md.Rezaul Karim Michel Dumontier Stefan Decker Luiz Olavo Bonino da Silva Santos Andre Dekker Fraunhofer Institute for Applied Information Technology(FIT) 53754 Sankt AugustinGermany RWTH Aachen University 52056 AachenGermany Department of Radiation Oncology(MAASTRO) GROW School for Oncology and Developmental BiologyMaastricht University Medical Center6200 MD MaastrichtThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science University of TübingenTubingenBaden-Württemberg 72076Germany Quantitative Biology Center University of TübingenTubingenBaden-Württemberg 72076Germany Institute for Translational Bioinformatics University of TubingenTubingenBaden-Württemberg 72076Germany Center for Bioinformatics University of TübingenGermany GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands

In recent years,as newer technologies have evolved around the healthcare ecosystem,more and more data have been *** analytics could power the data collected from numerous sources,both from healthcare institutions,or generated by individuals themselves via apps and devices,and lead to innovations in treatment and diagnosis of diseases;improve the care given to the patient;and empower citizens to participate in the decision-making process regarding their own health and ***,the sensitive nature of the health data prohibits healthcare organizations from sharing the *** Personal Health Train(PHT)is a novel approach,aiming to establish a distributed data analytics infrastructure enabling the(re)use of distributed healthcare data,while data owners stay in control of their own *** main principle of the PHT is that data remain in their original location,and analytical tasks visit data sources and execute the *** PHT provides a distributed,flexible approach to use data in a network of participants,incorporating the FAIR *** facilitates the responsible use of sensitive and/or personal data by adopting international principles and *** paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

关键词： Distributed analytics data reuse FAIR Health data Ethics and privacy

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big data Cost function Minimization

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Alignments of biomolecular contact maps

Alignments of biomolecular contact maps

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作者： Stadler, Peter F. Bioinformatics Group Department of Computer Science and Interdisciplinary Centre for Bioinformatics Universität Leipzig Härtelstraße 16-18 Leipzig04107 Germany Halle-Jena-Leipzig Competence Centre for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Centre for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 Wien1090 Austria Facultad de Ciencias Universidad National de Colombia Bogota Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here, we focus on contact maps, i.e. undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex-ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e. crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time. Moreover, the tree or forest alignments of RNA secondary structure can be understood as the alignments of ordered edge sets. © 2021 The Author(s).

关键词： Alignment

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