Visualization of Biomedical Data
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A Call for Public Archives for Biological Image Data Public Data Archives Are the Backbone of Modern Biological Research
comment A call for public archives for biological image data Public data archives are the backbone of modern biological research. Biomolecular archives are well established, but bioimaging resources lag behind them. The technology required for imaging archives is now available, thus enabling the creation of the frst public bioimage datasets. We present the rationale for the construction of bioimage archives and their associated databases to underpin the next revolution in bioinformatics discovery. Jan Ellenberg, Jason R. Swedlow, Mary Barlow, Charles E. Cook, Ugis Sarkans, Ardan Patwardhan, Alvis Brazma and Ewan Birney ince the mid-1970s it has been possible image data to encourage both reuse and in automation and throughput allow this to analyze the molecular composition extraction of knowledge. to be done in a systematic manner. We Sof living organisms, from the individual Despite the long history of biological expect that, for example, super-resolution units (nucleotides, amino acids, and imaging, the tools and resources for imaging will be applied across biological metabolites) to the macromolecules they collecting, managing, and sharing image and biomedical imaging; examples in are part of (DNA, RNA, and proteins), data are immature compared with those multidimensional imaging8 and high- as well as the interactions among available for sequence and 3D structure content screening9 have recently been these components1–3. The cost of such data. In the following sections we reported. It is very likely that imaging data measurements has decreased remarkably outline the current barriers to progress, volume and complexity will continue to over time, and technological development the technological developments that grow. Second, the emergence of powerful has widened their scope from investigations could provide solutions, and how those computer-based approaches for image of gene and transcript sequences (genomics/ infrastructure solutions can meet the interpretation, quantification, and modeling transcriptomics) to proteins (proteomics) outlined need. -
3 on the Nature of Biological Data
ON THE NATURE OF BIOLOGICAL DATA 35 3 On the Nature of Biological Data Twenty-first century biology will be a data-intensive enterprise. Laboratory data will continue to underpin biology’s tradition of being empirical and descriptive. In addition, they will provide confirming or disconfirming evidence for the various theories and models of biological phenomena that researchers build. Also, because 21st century biology will be a collective effort, it is critical that data be widely shareable and interoperable among diverse laboratories and computer systems. This chapter describes the nature of biological data and the requirements that scientists place on data so that they are useful. 3.1 DATA HETEROGENEITY An immense challenge—one of the most central facing 21st century biology—is that of managing the variety and complexity of data types, the hierarchy of biology, and the inevitable need to acquire data by a wide variety of modalities. Biological data come in many types. For instance, biological data may consist of the following:1 • Sequences. Sequence data, such as those associated with the DNA of various species, have grown enormously with the development of automated sequencing technology. In addition to the human genome, a variety of other genomes have been collected, covering organisms including bacteria, yeast, chicken, fruit flies, and mice.2 Other projects seek to characterize the genomes of all of the organisms living in a given ecosystem even without knowing all of them beforehand.3 Sequence data generally 1This discussion of data types draws heavily on H.V. Jagadish and F. Olken, eds., Data Management for the Biosciences, Report of the NSF/NLM Workshop of Data Management for Molecular and Cell Biology, February 2-3, 2003, Available at http:// www.eecs.umich.edu/~jag/wdmbio/wdmb_rpt.pdf. -
Data Visualization by Nils Gehlenborg
Data Visualization Nils Gehlenborg ([email protected]) Center for Biomedical Informatics / Harvard Medical School Cancer Program / Broad Institute of MIT and Harvard ISMB/ECCB 2011 http://www.biovis.net Flyers at ISCB booth! Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg A good sketch is better than a long speech. Napoleon Bonaparte Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Minard 1869 Napoleon’s March on Moscow Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 4 I believe when I see it. Unknown Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Anscombe 1973, The American Statistician Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 6 Anscombe 1973, The American Statistician Anscombe’s Quartet Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 7 Exploration: Hypothesis Generation trends gaps outliers clusters - A large data set is given and the goal is to learn something about it. - Visualization is employed to perform pattern detection using the human visual system. - The goal is to generate hypotheses that can be tested with statistical methods or follow-up experiments. Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 8 Visualization Use Cases Presentation Confirmation Exploration Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 9 Definition The use of computer-supported, interactive, visual representations of data to amplify cognition. Stu Card, Jock Mackinlay & Ben Shneiderman Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively.effectively. -
Visualization and Exploration of Transcriptomics Data Nils Gehlenborg
Visualization and Exploration of Transcriptomics Data 05 The identifier 800 year identifier Nils Gehlenborg Sidney Sussex College To celebrate our 800 year history an adaptation of the core identifier has been commissioned. This should be used on communications in the time period up to and including 2009. The 800 year identifier consists of three elements: the shield, the University of Cambridge logotype and the 800 years wording. It should not be redrawn, digitally manipulated or altered. The elements should not be A dissertation submitted to the University of Cambridge used independently and their relationship should for the degree of Doctor of Philosophy remain consistent. The 800 year identifier must always be reproduced from a digital master reference. This is available in eps, jpeg and gif format. Please ensure the appropriate artwork format is used. File formats European Molecular Biology Laboratory, eps: all professionally printed applications European Bioinformatics Institute, jpeg: Microsoft programmes Wellcome Trust Genome Campus, gif: online usage Hinxton, Cambridge, CB10 1SD, Colour United Kingdom. The 800 year identifier only appears in the five colour variants shown on this page. Email: [email protected] Black, Red Pantone 032, Yellow Pantone 109 and white October 12, 2010 shield with black (or white name). Single colour black or white. Please try to avoid any other colour combinations. Pantone 032 R237 G41 B57 Pantone 109 R254 G209 B0 To Maureen. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and acknowledgements. This dissertation is not substantially the same as any I have submit- ted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. -
Digital Scholarship Commons Presentation 09.24.14
A short introduction: Information visualizations in teaching & research Isabel Meirelles | [email protected] Associate Professor, Graphic Design, CAMD Digital Scholarship Commons NU Sept.24 Information design Infographics! Information visualization Data visualization Information design Infographics Graphical representations that aim at communicating information with the purpose to reveal patterns and relationships not known or not easily deduced without the aid of the visual presentation of information. ! Information visualization Data visualization “the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” Card et al. : Readings in Information Visualization: Using Vision to Think Information From Latin informare to give form or shape to, from in into + formare to form, from forma a form or shape + -ation indicating a process or condition The Oxford American Thesaurus of Current English Information Definition* Definition information = well-formed and meaningful data *Weak definition. The strong definition includes the further condition of truthfulness. Luciano Floridi (2010): Information, A Very Short Introduction Information taxonomy by Floridi M. Chen & L. Floridi (2012): An Analysis of Information in Visualization in Synthese, Springer (historical) intermezzo Visual/diagrammatic representations over history 9th-ct., France: M. Capella, De nuptiis,. c. 1310, England: Table of the Ten Commandments Planetary diagrams from the De Lisle Psalter To increase working memory 1582: Giordano -
When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data
Pacific Symposium on Biocomputing 2019 When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data Gamze Gürsoy* Computational Biology and Bioinformatics Program, Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, 06511, USA Email: [email protected] Arif Harmanci Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA Email: [email protected] Haixu Tang† School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47405, USA Email: [email protected] Erman Ayday Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 44106, USA Email: [email protected] Steven E. Brenner# University of California Berkeley, CA, 94720-3012, USA Email: [email protected] High-throughput technologies for biological data acquisition are advancing at an increasing pace. Most prominently, the decreasing cost of DNA sequencing has led to an exponential growth of sequence information, including individual human genomes. This session of the 2019 Pacific Symposium on Biocomputing presents the distinctive privacy and ethical challenges related to the generation, storage, processing, study, and sharing of individuals’ biological data generated by multitude of technologies including but not limited to genomics, proteomics, metagenomics, bioimaging, biosensors, and personal health trackers. The mission is to bring together computational biologists, experimental biologists, computer scientists, ethicists, and policy and lawmakers to share ideas, discuss the challenges related to biological data and privacy. Keywords: biological data privacy, genomics, genetic testing * This work is partially supported by NIH grant U01EB023686. † This work is partially supported by NIH grant U01EB023685 and NSF grant CNS-1408874. -
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration Among Sciences, Engineering, Arts, and Design
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors The MIT Press, Cambridge, Massachusetts and London, England Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors Cover image: “Map of Science Derived from Clickstream Data” (2009). Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the under-representation of the social sciences and humanities that is commonly found in citation data. © Johan Bollen. Used with permission. Originally published in Bollen, J., H. Van de Sompel, A. Hagberg, L. Bettencourt, R. Chute, et al. (2009), “Clickstream Data Yields High-Resolution Maps of Science.” PLoS ONE 4 (3): e4803. doi: 10.1371/journal.pone.0004803. 3 This material is based on work supported by the National Science Foundation under Grant No. 1142510, IIS, Human-Centered Computing, “Collaborative Research: EAGER: Network for Science, Engineering, Arts and Design (NSEAD).” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. © 2015 ISAST Published under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) eISBN: 978-0-262-75863-5 CONTENTS Acknowledgments ......................................................................................i SEAD White Papers Committees ...............................................................ii Introduction ...............................................................................................1 1. SEAD White Papers Methodology ......................................................13 2. -
From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2*
Alyass et al. BMC Medical Genomics (2015) 8:33 DOI 10.1186/s12920-015-0108-y REVIEW Open Access From big data analysis to personalized medicine for all: challenges and opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2* Abstract Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine. Keywords: Big data, Omics, Personalized medicine, High-throughput technologies, Cloud computing, Integrative methods, High-dimensionality Introduction of biology and medicine [5, 6]. The use of deterministic Access to large omics (genomics, transcriptomics, proteo- networks for normal and abnormal phenotypes are mics, epigenomic, metagenomics, metabolomics, nutrio- thought to allow for the proactive maintenance of wellness mics, etc.) data has revolutionized biology and has led to specific to the individual, that is predictive, preventive, the emergence of systems biology for a better understand- personalized, and participatory medicine (P4, or more ing of biological mechanisms. -
School of Architecture 2016–2017 School of Architecture School Of
BULLETIN OF YALE UNIVERSITY BULLETIN OF YALE BULLETIN OF YALE UNIVERSITY Periodicals postage paid New Haven ct 06520-8227 New Haven, Connecticut School of Architecture 2016–2017 School of Architecture 2016 –2017 BULLETIN OF YALE UNIVERSITY Series 112 Number 4 June 30, 2016 BULLETIN OF YALE UNIVERSITY Series 112 Number 4 June 30, 2016 (USPS 078-500) The University is committed to basing judgments concerning the admission, education, is published seventeen times a year (one time in May and October; three times in June and employment of individuals upon their qualifications and abilities and a∞rmatively and September; four times in July; five times in August) by Yale University, 2 Whitney seeks to attract to its faculty, sta≠, and student body qualified persons of diverse back- Avenue, New Haven CT 0651o. Periodicals postage paid at New Haven, Connecticut. grounds. In accordance with this policy and as delineated by federal and Connecticut law, Yale does not discriminate in admissions, educational programs, or employment against Postmaster: Send address changes to Bulletin of Yale University, any individual on account of that individual’s sex, race, color, religion, age, disability, PO Box 208227, New Haven CT 06520-8227 status as a protected veteran, or national or ethnic origin; nor does Yale discriminate on the basis of sexual orientation or gender identity or expression. Managing Editor: Kimberly M. Go≠-Crews University policy is committed to a∞rmative action under law in employment of Editor: Lesley K. Baier women, minority group members, individuals with disabilities, and protected veterans. PO Box 208230, New Haven CT 06520-8230 Inquiries concerning these policies may be referred to Valarie Stanley, Director of the O∞ce for Equal Opportunity Programs, 221 Whitney Avenue, 3rd Floor, 203.432.0849. -
Visualizing Biological Data
THIS MONTH y y x x 22 1 22 1 21 21 POINTS OF VIEW 20 20 2 2 19 19 18 18 3 3 17 17 Visualizing biological 16 16 4 4 15 15 data 14 5 14 5 Data visualization is increasingly important, 13 6 13 6 12 7 12 7 8 8 9 but it requires clear objectives and improved 11 11 9 10 10 implementation. Figure 1 | Visualization of whole-genome rearrangement. Representative Researchers today have access to an unprecedented amount of Circos plots1 of whole-genome sequence data from two different tumors data. The challenge is to benefit from this abundance without showing gene duplications and chromosome rearrangements. The outer ring depicts chromosomes arranged end to end. The inner ring displays being overwhelmed. Data visualization for efficient exploration copy-number data in green and interchromosomal translocations in purple. and effective communication is integral to scientific progress. Reprinted from ref. 2 with permission from Elsevier. For visualization to continue to be an important tool for discov- ery, its practitioners need to be present as members of research teams. rapidly turn sketches into software prototypes at a pace relevant One of the goals of data visualization is to enable people to to the research: as data types and research questions evolve, there explore and explain data through interactive software that takes is a constant need to refine and adapt visualizations. One funding advantage of human beings’ ability to recognize patterns. Its suc- mechanism that has enabled an integrated focus on visualiza- cess depends on the development of methods and techniques to tion in the context of biological research is supplemental sup- transform information into a visual form for comprehension. -
The Scientist As Illustrator
TREIMM 1262 No. of Pages 4 Special Issue: and technology. Leonardo's careful study of visual communicators as it ever was Communicating Science of human anatomy and interest in propor- in the past. tions was demonstrated in the Vitruvian Scientific Life man, one of his most famous drawings. Drawing to Understand In molecular and cellular biology, our The Scientist as With the rise of the printing press, scien- understanding of processes is typically Illustrator tists could reach a larger audience with based on experimental data that are indi- whom they could share their findings. In rect, abstract, and collected by different [1_TD$IF] 1, Sidereus Nuncius, Galileo Galilei was the laboratories using an assortment of tech- Janet H. Iwasa * first to publish observations made using a niques over the course of decades. To telescope. A polymath who excelled in understand processes that are taking fi Pro ciency in art and illustration astronomy, mathematics, and physics, place at scales smaller than the wave- was once considered an essential Galileo had also studied medicine and length of light, biologists must synthesize skill for biologists, because text had once considered a career in painting diverse data to generate a working model alone often could not suffice to [1]. His manuscript included over 70 or hypothesis. In contrast with scientists of describe observations of biological detailed illustrations, including the first the past, we must rely on visualizations not systems. With modern imaging realistic depictions of the craggy and pit- to record and share our observations, but technology, it is no longer neces- ted surface of the moon. -
Personalized Medicine in Big Data
INTERNATIONAL JOURNAL FOR RESEARCH IN EMERGING SCIENCE AND TECHNOLOGY E-ISSN: 2349-7610 Personalized Medicine in Big Data Mr.A.Kesavan, M.C.A., M.Phil., Dr.C.Jayakumari, M.C.A., Ph.D., Mr.A.Kesavan, M.C.A, M.Phil., Research Scholar, Bharathiar University, India. [email protected] Dr.C.Jayakumari, M.C.A, Ph.D., Associate Professor, Middle East College, Oman, [email protected] ABSTRACT The rapid and ongoing digitalization of society leads to an exponential growth of both structured and unstructured data, so-called Big Data. This wealth of information opens the door to the development of more advanced personalized medicine technologies. The analysis of log data from such applications and wearables provide the opportunity to personalize and to improve their strength and long-term use. Personalized medicine is the use of a patient’s genomic information to form individualized prevention, diagnosis, and treatment plans to combat disease. Keywords: Big Data, Personalized Medicine, Omics Data, High-throughput technologies, Integrative methods, High- dimensionality. 1. INTRODUCTION Big Data refers to the practice of combining huge volumes of The overarching goal of “Personalized Medicine” is to create a diversely sourced information and analyzing them, using more framework that leverage patient EHRs and Omics data to sophisticated algorithms to inform decisions. Big Data relies facilitate clinical decision-making that is predictive, not only on the increasing ability of technology to support the personalized, preventive and participatory [4]. The emphasis is collection and storage of large amounts of data, but also on its to customize medical treatment based on the individual ability to analyses, understand and take advantage of the full characteristics of patients and nature of their disease, value of data.