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Intelligence and Security Informatics
CHAPTER 6 Intelligence and Security Informatics Hsinchun Chen and Jennifer Xu University of Arizona ISI: Challenges and Research Framework The tragic events of September 11,2001, and the subsequent anthrax scare had profound effects on many aspects of society. Terrorism has become the most significant threat to domestic security because of its potential to bring massive damage to the nation’s infrastructure and economy. In response to this challenge, federal authorities are actively implementing comprehensive strategies and measures to achieve the three objectives identified in the “National Strategy for Homeland Security” report (U.S. Office of Homeland Security, 2002): (1)preventing future terrorist attacks, (2) reducing the nation’s vulnerability, and (3) minimizing the damage and expediting recovery from attacks that occur. State and local law enforcement agencies, likewise, have become more vigilant about criminal activities that can threaten public safety and national security. Academics in the natural sciences, computational science, informa- tion science, social sciences, engineering, medicine, and many other fields have also been called upon to help enhance the government’s capa- bilities to fight terrorism and other crime. Science and technology have been identified in the “National Strategy for Homeland Security” report as the keys to winning the new counter-terrorism war (U. S. Office of Homeland Security, 2002). In particular, it is believed that information technology and information management will play indispensable roles in making the nation safer (Cronin, 2005; Davies, 2002; National Research Council, 2002) by supporting intelligence and knowledge dis- covery through collecting, processing, analyzing, and utilizing terrorism- and crime-related data (Badiru, Karasz, & Holloway, 1988; Chen, Miranda, Zeng, Demchak, Schroeder, & Madhusudan, 2003; Chen, Moore, Zeng, & Leavitt, 2004). -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
Ploidetect Enables Pan-Cancer Analysis of the Causes and Impacts of Chromosomal Instability
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.06.455329; this version posted August 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Ploidetect enables pan-cancer analysis of the causes and impacts of chromosomal instability Luka Culibrk1,2, Jasleen K. Grewal1,2, Erin D. Pleasance1, Laura Williamson1, Karen Mungall1, Janessa Laskin3, Marco A. Marra1,4, and Steven J.M. Jones1,4, 1Canada’s Michael Smith Genome Sciences Center at BC Cancer, Vancouver, British Columbia, Canada 2Bioinformatics training program, University of British Columbia, Vancouver, British Columbia, Canada 3Department of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada 4Department of Medical Genetics, Faculty of Medicine, Vancouver, British Columbia, Canada Cancers routinely exhibit chromosomal instability, resulting in tumors mutate, these variants are considerably more difficult the accumulation of changes in the abundance of genomic ma- to detect accurately compared to other types of mutations terial, known as copy number variants (CNVs). Unfortunately, and consequently they may represent an under-explored the detection of these variants in cancer genomes is difficult. We facet of tumor biology. 20 developed Ploidetect, a software package that effectively iden- While small mutations can be determined through base tifies CNVs within whole-genome sequenced tumors. Ploidetect changes embedded within aligned sequence reads, CNVs was more sensitive to CNVs in cancer related genes within ad- are variations in DNA quantity and are typically determined vanced, pre-treated metastatic cancers than other tools, while also segmenting the most contiguously. -
BIOINFORMATICS APPLICATIONS NOTE Pages 380-381
Vol. 14 no. 4 1998 BIOINFORMATICS APPLICATIONS NOTE Pages 380-381 MView: a web-compatible database search or multiple alignment viewer NigelP. Brown, ChristopheLeroy and Chris Sander European Bioinformatics Institute (EMBLĆEBI), Wellcome Genome Campus, CambridgeCB10 1SD, UK Received on December 10, 1997; revised and accepted on January 15, 1998 Abstract may be hyperlinked to the SRS system (Etzold et al., 1996), a text field, a field of scoring information from searches, and Summary: MView is a tool for converting the results of a a field reporting the per cent identity of each sequence with sequence database search into the form of a coloured multiple respect to a preferred sequence in the alignment, usually the alignment of hits stacked against the query. Alternatively, an query in the case of a search. existing multiple alignment can be processed. In either case, Multiple alignments require minimal parsing and are the output is simply HTML, so the result is platform independent and does not require a separate application or subjected only to formatting stages. Search hits are first applet to be loaded. stacked against the ungapped query sequence and require Availability: Free from http://www.sander.ebi.ac.uk/mview/ special processing. Ungapped search (e.g. BLAST) hit subject to copyright restrictions. fragments are assembled into a single string by overlaying Contact: [email protected] them preferentially by score onto a template string, while gapped search (e.g. FASTA) hits have columns corresponding Often when running FASTA (Pearson, 1990) or BLAST to query gaps excised. Consequently, the stacked alignment is (Altschul et al., 1990), it is desired to visualize the database a patchwork of reconstituted sequences that nevertheless is hits stacked against the query sequence. -
Visualization and Analytics Tools for Infectious Disease Epidemiology: 7 5 a Systematic Review
YJBIN 2161 No. of Pages 12, Model 5G 18 April 2014 Journal of Biomedical Informatics xxx (2014) xxx–xxx 1 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin 2 Methodological Review 6 4 Visualization and analytics tools for infectious disease epidemiology: 7 5 A systematic review a a b c d 8 Q1 Lauren N. Carroll , Alan P. Au , Landon Todd Detwiler , Tsung-chieh Fu , Ian S. Painter , a,d,⇑ 9 Q2 Neil F. Abernethy 10 Q3 a Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St., Box 358047, Seattle, WA 98109, United States 11 b Department of Biological Structure, University of Washington, 1959 NE Pacific St., Box 357420, United States 12 c Department of Epidemiology, University of Washington, 850 Republican St., Box 358047, Seattle, WA 98109, United States 13 d Department of Health Services, University of Washington, 1959 NE Pacific St., Box 359442, Seattle, WA 98195, United States 1415 1617 article info abstract 3219 20 Article history: Background: A myriad of new tools and algorithms have been developed to help public health professionals 33 21 Received 13 September 2013 analyze and visualize the complex data used in infectious disease control. To better understand approaches 34 22 Accepted 3 April 2014 to meet these users’ information needs, we conducted a systematic literature review focused on the land- 35 23 Available online xxxx scape of infectious disease visualization tools for public health professionals, with a special emphasis on 36 geographic information systems (GIS), molecular epidemiology, and social network analysis. -
Are You an Invited Speaker? a Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics
Are You an Invited Speaker? A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics Senator Jeong, Sungin Lee, and Hong-Gee Kim Biomedical Knowledge Engineering Laboratory, Seoul National University, 28–22 YeonGeon Dong, Jongno Gu, Seoul 110–749, Korea. E-mail: {senator, sunginlee, hgkim}@snu.ac.kr Participating in scholarly events (e.g., conferences, work- evaluation, but it would be hard to claim that they have pro- shops, etc.) as an elite-group member such as an orga- vided comprehensive lists of evaluation measurements. This nizing committee chair or member, program committee article aims not to provide such lists but to add to the current chair or member, session chair, invited speaker, or award winner is beneficial to a researcher’s career develop- practices an alternative metric that complements existing per- ment.The objective of this study is to investigate whether formance measures to give a more comprehensive picture of elite-group membership for scholarly events is represen- scholars’ performance. tative of scholars’ prominence, and which elite group is By one definition (Jeong, 2008), a scholarly event is the most prestigious. We collected data about 15 global “a sequentially and spatially organized collection of schol- (excluding regional) bioinformatics scholarly events held in 2007. We sampled (via stratified random sampling) ars’ interactions with the intention of delivering and shar- participants from elite groups in each event. Then, bib- ing knowledge, exchanging research ideas, and performing liometric indicators (total citations and h index) of seven related activities.” As such, scholarly events are communica- elite groups and a non-elite group, consisting of authors tion channels from which our new evaluation tool can draw who submitted at least one paper to an event but were its supporting evidence. -
Information Management Technology
Information Management Technology 12 May 2006 | News Image not found or type unknown Information Management Technology Laboratory informatics has a much broader application area-scientific research from drug discovery to production. Information science or "informatics" is the science of information. It is often studied as a branch of computer science and information technology and is related to database, ontology and software engineering. Informatics is primarily concerned with: Creating and structuring data Management, storage and retrieval of data Distribution and transfer of information Informatics is about the transformation of information by computation or communication; by machines or people. It is the intersection of artificial intelligence, cognitive science, computing science and related fields. Informatics focuses on understanding business or scientific research challenges and applying information technology as the solution-tackling the problem first rather than technology first. It combines software applications, search technology, database storage and tools for digitally-enabled collaboration. Laboratory informatics Laboratory informatics is the specialized application of information technology to maximize laboratory operations, particularly in analytical, production and research and development. Laboratory informatics encompasses data acquisition, data processing, data analysis and long-term archiving, electronic laboratory notebooks, laboratory information management, laboratory automation, scientific data management, document -
Full List of PCAWG Consortium Working Groups and Writing
Supplementary information to: Genomics: data sharing needs an international code of conduct To accompany a Comment published in Nature 578, 31–33 (2020) https://www.nature.com/articles/d41586-020-00082-9 By Mark Phillips, Fruzsina Molnár-Gábor, Jan O. Korbel, Adrian Thorogood, Yann Joly, Don Chalmers, David Townend & Bartha M. Knoppers for the PCAWG Consortium. SUPPLEMENTARY INFORMATION | NATURE | 1 The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium Working Groups PCAWG Steering committee 1,2 3,4,5,6 7,8 9,10 Peter J Campbell# , Gad Getz# , Jan O Korbel# , Lincoln D Stein# and Joshua M Stuart#11,12 PCAWG Head of project management Jennifer L Jennings13 PCAWG Executive committee 14 15 16 17 18 Sultan T Al-Sedairy , Axel Aretz , Cindy Bell , Miguel Betancourt , Christiane Buchholz , 19 20 21 22 23 Fabien Calvo , Christine Chomienne , Michael Dunn , Stuart Edmonds , Eric Green , Shailja 24 23 25 13 26 Gupta , Carolyn M Hutter , Karine Jegalian , Jennifer L Jennings , Nic Jones , Hyung-Lae 27 28,29,30 31 32 32 26 Kim , Youyong Lu , Hitoshi Nakagama , Gerd Nettekoven , Laura Planko , David Scott , 3 3,34 35 9,10 1 35 Tatsuhiro Shibata , Kiyo Shimizu , Lincoln D Stein# , Michael R Stratton , Takashi Yugawa , 36,37 24 38 39 Giampaolo Tortora , K VijayRaghavan , Huanming Yang and Jean C Zenklusen PCAWG Ethics and Legal Working Group 40 41 41 42 41 Don Chalmers# , Yann Joly , Bartha M Knoppers# , Fruzsina -
Laboratory Informatics Guide 2014
LABORATORY INFORMATICS GUIDE 2014 www.scientific-computing.com/lig2014 FROM THE PUBLISHERS OF subsidiaries. BREAKTHROUGH MOMENT #46 QUERYING YOUR DATA AND INSTANTLY VISUALIZING THE ANSWER © 2011 PerkinElmer, Inc. 400280_06. All trademarks or registered are the property of and/or its Scientifi c breakthroughs don’t happen every day. But with the TIBCO Spotfi re® platform, they just might happen more often. TIBCO Spotfi re® software visualizes your data in stunning, impactful analyses—giving you immediate insights and even answering questions you haven’t posed yet. With such a powerful tool, you can take complete control of your data: • Combine data from multiple sources—chemical structures, text, numbers, images, chemical properties, biological assays, and more. • Identify new relationships, isolate signifi cant outliers, and easily spot trends and patterns. TIBCO Spotfi re® software connects to data sources with just a few clicks—and runs visualizations instantly. It’s that easy. Download your free trial and get started on your next breakthrough today. www.perkinelmer.com/spotfi re-scientifi ccomputingworld Created By: WHM Creative Production: Sarah Pruett / 206.313.1252 Stock: TBD Job: CID3639 PerkinElmer TIBCO Spotfi re Breakthrough Print Ad Inks: CMYK - Match to PMS swatches for PMS299C, PMS 369C, Date: 12/02/13 PMS 423C, PMS 286C, PMS 144C Finish Size: 213mm x 282mm contents | LABORATORY INFORMATICS GUIDE 2014 WELCOME There is a paradox at the heart of this year’s Laboratory Informatics Guide. On the one hand, we have an article showing how technology developed for the consumer market can help drive down costs and improve efficiencies in the work of the analytical laboratory. -
Component 5: History of Health Information Technology in the U.S
Component 5: History of Health Information Technology in the U.S. Instructor Manual Version 3.0/Spring 2012 Notes to Instructors This Instructor Manual is a resource for instructors using this component. Each component is broken down into units, which include the following elements: • Learning objectives • Suggested student readings, texts, reference links to supplement the narrated PowerPoint slides • Lectures (voiceover PowerPoint in Flash format); PowerPoint slides (Microsoft PowerPoint format), lecture transcripts (Microsoft Word format); and audio files (MP3 format) for each lecture • Self-assessment questions reflecting Unit Objectives with answer keys and/or expected outcomes • Application Activities (e.g., discussion questions, assignments, projects) with instructor guidelines, answer keys and/or expected outcomes Health IT Workforce Curriculum History of Health Information 2 Technology in the U.S. Version 3.0/Spring 2012 This material was developed by The University of Alabama at Birmingham, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information echnologyT under Award Number 1U24OC000023 Contents Notes to Instructors ...................................................................................2 Disclaimer ..................................................................................................5 Component Overview ................................................................................6 Component Objectives ..............................................................................6 -
Medical Informatics
MEDICAL INFORMATICS Knowledge Management and Data Mining in Biomedicine INTEGRATED SERIES IN INFORMATION SYSTEMS Series Editors Professor Ramesh Sharda Prof. Dr. Stefan Vo13 Oklahoma State University Universitat Hamburg Other published titles in the series: E-BUSINESS MANAGEMENT: Integration of Web Technologies with Business Models1 Michael J. Shaw VIRTUAL CORPORATE UNIVERSITIES: A Matrix of Knowledge and Learning for the New Digital DawdWalter R.J. Baets & Gert Van der Linden SCALABLE ENTERPRISE SYSTEMS: An Introduction to Recent Advances1 edited by Vittal Prabhu, Soundar Kumara, Manjunath Kamath LEGAL PROGRAMMING: Legal Compliance for RFID and Software Agent Ecosystems in Retail Processes and Beyond1 Brian Subirana and Malcolm Bain LOGICAL DATA MODELING: What It Is and How To Do It1 Alan Chmura and J. Mark Heumann DESIGNING AND EVALUATING E-MANAGEMENT DECISION TOOLS: The Integration of Decision and Negotiation Models into Internet-Multimedia Technologies1 Giampiero E.G. Beroggi INFORMATION AND MANAGEMENT SYSTEMS FOR PRODUCT CUSTOMIZATIONI Blecker, Friedrich, Kaluza, Abdelkafi & Kreutler MEDICAL INFORMATICS Knowledge Management and Data Mining in Biomedicine edited by Hsinchun Chen Sherrilynne S. Fuller Carol Friedman William Hersh - Springer Hsinchun Chen Sherrilynne S. Fuller The University of Arizona, USA University of Washington, USA Carol Friedman William Hersh Columbia University, USA Oregon Health & Science Univ., USA Library of Congress Cataloging-in-Publication Data A C.I.P. Catalogue record for this book is available from the Library of Congress. ISBN-10: 0-387-2438 1-X (HB) ISBN- 10: 0-387-25739-X (e-book) ISBN- 13: 978-0387-24381-8 (HB) ISBN- 13: 978-0387-25739-6 (e-book) O 2005 by Springer Science+Business Media, Inc. -
GAO-17-14 Accessible Version, OPEN INNOVATION: Practices to Engage
United States Government Accountability Office Report to Congr essional Committees October 2016 OPEN INNOVATION Practices to Engage Citizens and Effectively Implement Federal Initiatives Accessible Version GAO-17-14 October 2016 OPEN INNOVATION Practices to Engage Citizens and Effectively Implement Federal Initiatives Highlights of GAO-17-14, a report to congressional committees Why GAO Did This Study What GAO Found To address the complex and Open innovation involves using various tools and approaches to harness the crosscutting challenges facing the ideas, expertise, and resources of those outside an organization to address an federal government, agencies need to issue or achieve specific goals. GAO found that federal agencies have frequently effectively engage and collaborate with used five open innovation strategies to collaborate with citizens and external those in the private, nonprofit, and stakeholders, and encourage their participation in agency initiatives. academic sectors, other levels of government, and citizens. Agencies Descriptions of Open Innovation Strategies Used by Federal Agencies are increasingly using open innovation strategies for these purposes. The GPRA Modernization Act of 2010 (GPRAMA) requires federal agencies to identify strategies and resources they will use to achieve their goals. GPRAMA also requires GAO to periodically review how implementation of its requirements is affecting agency performance. This report identifies and illustrates practices that help agencies effectively implement open innovation strategies, and how the use of those strategies has affected agency performance and opportunities for citizen engagement. GAO identified seven practices that agencies can use to effectively implement To identify these practices, GAO initiatives that involve the use of these strategies: analyzed relevant federal guidance and academic literature, and · Select the strategy appropriate for the purpose of engaging the public and interviewed open innovation experts.