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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

arXiv

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arXiv 2020年

作者： Ali, Sharib Dmitrieva, Mariia Ghatwary, Noha Bano, Sophia Polat, Gorkem Temizel, Alptekin Krenzer, Adrian Hekalo, Amar Guo, Yun Bo Matuszewski, Bogdan Gridach, Mourad Voiculescu, Irina Yoganand, Vishnusai Chavan, Arnav Raj, Aryan Nguyen, Nhan T. Tran, Dat Q. Huynh, Le Duy Boutry, Nicolas Rezvy, Shahadate Chen, Haijian Choi, Yoon Ho Subramanian, Anand Balasubramanian, Velmurugan Gao, Xiaohong W. Hu, Hongyu Liao, Yusheng Stoyanov, Danail Daul, Christian Realdon, Stefano Cannizzaro, Renato Lamarque, Dominique Tran-Nguyen, Terry Bailey, Adam Braden, Barbara East, James Rittscher, Jens Institute of Biomedical Engineering and Big Data Institute Old Road Campus University of Oxford Oxford United Kingdom Translational Gastroenterology Unit Experimental Medicine Div. John Radcliffe Hospital University of Oxford Oxford United Kingdom Oxford NIHR Biomedical Research Centre Oxford United Kingdom Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré France Instituto Onclologico Veneto IOV-IRCCS Padova Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Science University College London London United Kingdom CRAN UMR 7039 University of Lorraine CNRS Nancy France Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Artificial Intelligence and Knowledge Systems University of Würzburg Germany School of Engineering University of Central Lancashire United Kingdom Department of Computer Science University of Oxford United Kingdom Mimyk Medical Simulations Pvt Ltd Indian Institute of Science Bengaluru India Dhanbad India Hanoi Viet Nam Le Kremlin-BicêtreF-94270 France School of Science and Technology Middlesex University London United Kingdom Department of Computer Science School of Informatics Xiamen University China Sungkyunkwan University Seoul Korea Republic of Claritrics India Pvt Ltd Chennai India School of Medical Science and Technology Indian Institute of Technology KharagpurWest Bengal India Shanghai Jiaotong University Shanghai China Ibn Zohr University Computer Science HIT Agadir Morocco

The Endoscopy computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. © 2020, CC BY-NC-SA.

关键词： Endoscopy

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Prioritizing Strategies for Building the Resilience of Public Health Systems to Disasters Across Multiple Communities and Countries

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International Journal of Disaster Risk science 2024年第1期15卷 1-17页

作者： Benjamin J.Ryan Mayumi Kako Shelby Garner Rok Fink Ismail Tayfur Jonathan Abrahams Sanjaya Bhatia Adriana Campelo Matthew Fendt Alicia Fontenot Nahuel Arenas Garcia Tim Hatch Ryoma Kayano LaShonda Malrey‑Horne Makiko MacDermot Md Moshiur Rahman Chaverle Noel Shuhei Nomura Jeremy P.Novak Maria Opazo Kendell Oliver Luciana Peters Sohel Rahman PerihanŞimşek Andrew Stricklin Raymond Swienton Bryan W.Brooks Frist College of Medicine Belmont UniversityNashvilleTN 37212USA Department of Environmental Science Environmental Health Science ProgramCollege of Arts and SciencesBaylor UniversityWacoTX 76798USA Division of Nursing Science School of Biomedical and Health SciencesHiroshima UniversityHiroshima 734-8553Japan Gordon E.Inman College of Health Sciences and Nursing Belmont UniversityNashvilleTN 37212USA Faculty of Health Sciences University of Ljubljana1000 LjubljanaSlovenia Department of Emergency Medicine University of Health Sciences34668 IstanbulTurkey Monash University Accident Research Center MelbourneVIC 3800Australia Global Education and Training Institute United Nations Ofce for Disaster Risk ReductionIncheon 22004Republic of Korea Regional Ofce for the Americas and Caribbean United Nations Ofce for Disaster Risk ReductionPanama City 0843-03441Panama Engineering and Computer Science Baylor UniversityWacoTX 76798USA Department of Public Health Robbins College of Health and Human SciencesBaylor UniversityWacoTX 76798USA Alabama Department of Public Health MontgomeryAL 36092USA World Health Organization Centre for Health Development(WHO Kobe Centre) Kobe 651-0073Japan Waco-McLennan County Public Health District WacoTX 76707USA Department of Health Policy and Management School of MedicineKeio UniversityTokyo 160-8582Japan Department of Global Health Policy Graduate School of MedicineThe University of TokyoTokyo 113-0033Japan Tokyo Foundation for Policy Research Tokyo 106-6234Japan Global Development College BrisbaneQLD 4000Australia Municipality of Pudahuel 13124 PudahuelChile City Hall of Porto Alegre Porto Alegre 90010-170Brazil Ministry of Health and Family Welfare Government of the People’s Republic of BangladeshDhaka 1000Bangladesh Department of Emergency Aid and Disaster Management Trabzon University61335 TrabzonTurkey Department of Emergency Medicine UT SouthwesternDallasTX 75390USA

The COVID-19 pandemic highlighted the urgent need to strengthen public health *** response,the United Nations Disaster Risk Reduction(UNDRR) Public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple *** aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system *** conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training Institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience *** were drawn from 70 strategies identified from 13 distinct workshops in eight *** two surveys,23 strategies were *** received ratings of "High" or Very High" from89% of *** related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of *** Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system *** leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

关键词： Health system resilience Public policy Resilience scorecard Workshops

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Nuclear cardiology image enhancement using wavelet transform analysis

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Physica Medica 2014年 30卷 e30-e30页

作者： A. Poulakis S. Angelakis G. Fountos G.S. Panagiotakis I. Asteriadis I. Valais Department of Biomedical Engineering Technological Educational Institute of Athens 12210 Athens Greece Biomedical Informatics and Technology Graduate Program School of Computer Science University of Crete Department of Medical Physics Medical School University of Patras 265 00 Patras Greece Department of Nuclear Medicine General Hospital of Kavala 65 500 Kavala Greece

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Polytomous item ordering theory based on ramsay's kernel smoothing nonparametric item response theory

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ICIC Express Letters 2012年第6期6卷 1537-1542页

作者： Liu, Hsiang-Chuan Wu, Shih-Neng Sheu, Tian-Wei Chung, Yen-Tung Tsai, Hsien-Chang Department of Biomedical Informatics Asia University No. 500 Lioufeng Rd Wufeng Taichung 41354 Taiwan Department of Computer Science and Information Engineering Asia University No. 500 Lioufeng Rd Wufeng Taichung 41354 Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University of Education No. 140 Min-Shen Rd Taichung 40306 Taiwan Foundatial Education Center National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Taichung 41170 Taiwan Department of Biology National Changhua University of Education No. 1 Jin-De Rd Changhua 50007 Taiwan

Our previous work, Liu's polytomous ordering theory can be used for any testing with homogeneous or heterogeneous polytomous response. It is more useful than other well-known polytomous ordering theories. However, it can only be used to detect item ordering relationships for a group of subjects, not for any personal of subject. To overcome this drawback, in this paper, the above-mentioned theory is improved by integrating Ramsay's kernel smoothing nonparametric item response theory which can be used to estimate the joint probability of any two items, and construct \personal item ordering structures without the local independence assumption. A computer program is developed for computing and drawing item structures. A calculus test example is also provided in this paper to illustrate the advantages of the proposed methods. © 2012 ICIC International.

关键词： Calculations

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Microchip-Based Structure Determination of Small Proteins Using Cryo-EM

SSRN

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SSRN 2020年

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University ParkPA16802 United States Materials Research Institute Pennsylvania State University University ParkPA16802 United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States RayBiotech Life Peachtree CornersGA30092 United States Applications Science Protochips Inc MorrisvilleNC27560 United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low molecular-weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal “top hat” motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. © 2020, The Authors. All rights reserved.

关键词： Proteins

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The Form of High-Performance Computing: A Survey

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IOP Conference Series: Materials science and engineering 2019年第5期662卷

作者： Priati Assiroj H L H S Warnars R Kosala B Ranti S Supangat A I Kistijantoro E Abdurrachman Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Dept. Buana Perjuangan University Karawang Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Department Bina Nusantara University Jakarta Information System Technology Department Institut Teknologi Bandung School of Electronics Engineering and Informatics Institut Teknologi Bandung

Computational technology is an important thing that needed in the whole of human life. Along with the development of computer technology, there are a significant data created and wait to be processed; we will get more advantages if it well-processed, if not we will see the data explosion, the data is just a useless stack. Research mitigates the other research paper and makes a conclusion and a report by PRISMA method. The development of computer technology will support the development of an organization. Considering any factors in choosing the right and suitable high-performance computer resource due to our organization is a must. The existing high-performance computing forms are Supercomputer, Grid Computing, Cluster Computing, and Cloud Computing. Based on this work, the last form of high-performance computing, Cloud Computing is one of the most used forms thus we mitigate its advantages and disadvantages. Recently, to process this vast data, we must have a high-performance computer and to provide it, this is not a cheap resource.

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Graph Artificial Intelligence in Medicine

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biomedical Data science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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Cloud computing model in higher education

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IOP Conference Series: Materials science and engineering 2020年第1期852卷

作者： Bagus Mulyawan Raymond Kosala Benny Ranti Suhono Harso Supangkat Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Universitas Tarumanagara Jakarta 11440 Computer Science Department Faculty of Computer Science Universitas Indonesia Indonesia 16424 Schoolof Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.

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