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You are here Biopharmaceutical/ Genomic Glossaries homepage/Search > Informatics for pharmaceuticals Informatics Overview
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to terms in these glossaries: Informatics Map
Site Map Information technology that facilitates the collection, analysis, and dissemination of huge amounts of molecular and clinical data is essential to the advancement and increased adoption of molecular medicine. caBigTM and Molecular Medicine, NCI, NIH 3D-QSAR Three-dimensional quantitative structure-activity relationships: Involves the analysis of the quantitative relationship between the biological activity of a set of compounds and their three- dimensional properties using statistical correlation methods. IUPAC Computational Drug discovery informatics applied clinical informatics: See under clinical informatics bioinformatics: Store, manage, retrieve, analyze and integrate vast amounts of genomic data being produced globally. Today embraces protein structure analysis, gene and protein functional information, data from patients, pre- clinical and clinical trials and metabolic pathways of numerous species. Bioinformatics
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BioIT World
Conference & Expo: Bio-IT World
Conference & Expo April 24-26, 2012 • Boston, MA Program | Register | Download Brochure
new trends in data generation, knowledge management, and information technology in life sciences and drug development, including best practice case studies and joint partner presentations relevant to the technologies, research, and regulatory issues of life science, pharmaceutical, clinical and IT professionals. BioIT World Weekly Update: BioIT http://www.bio-itworldweekly.com/category/1/bio-it/ biomedical informatics: The science underlying the acquisition, maintenance, retrieval, and application of biomedical knowledge and information to improve patient care, medical education, and health sciences research. John Gennari, Washington Univ. 2002 http://faculty.washington.edu/gennari/MedicalInformaticsDef.html bleeding edge: (General industry usage) Synonym for "cutting edge," with an added implication of the pioneer's vulnerability. Ex: "We're really on the bleeding edge with this product. Hope it sells through." Being "edgy" is still, however, a desirable Microsoft quality. Ken Barnes et. al, Microsoft Lexicon or Microspeak made easier, 1995-1998 http://www.cinepad.com/mslex.htm Research bottom-up: The classical reductionist approach to biology which aims to examine the smallest units to gain insight into the larger ones. Mendelian genetics, which looks at single genes, is a bottom- up approach. Compare top- down. Research
cancerinformatics:
cheminformatics: partnerships and collaborations to further drug discovery, open source chemistry, recent advances in cheminformatics, modeling for safety, using biological and chemical information to guide hit-to-lead phase and lead optimization, and repurposing drugs by applying 21st century tools to find new targets. BioIT World Track 5 Cheminformatics April 2010 Boston MA Order CD Cheminformatics and chemoinformatics are alternate spellings. Chemoinformatics originally predominated, but cheminformatics now seems to be the most prevalent spelling. See FAQ question #3. Cheminformatics clinical informatics:
Integration of clinical workflow and business strategies of
any healthcare organization will spell success for the providers of the future.
Efficient exchange of data and information is essential for this merger, and
information technology is the tool with which to accomplish the consolidation.
Clinical Informatics is the practice evolving from this need in healthcare.
HIMSS Clinical informatics http://www.himss.org/ASP/topics_clinicalInformatics.asp
Clinical informatics contains
two major divisions. The first relates to all those aspects of clinical
informatics whose objective is the application of informatics and information
technology to deliver healthcare services. At times, this has also been referred
to as applied clinical informatics. Despite some variations, AMIA
considers informatics when used for healthcare delivery to be essentially the
same regardless of the health professional group involved whether dentist,
pharmacist, physician, nurse, or other health professional. The other branch relates to clinical research informatics. Its primary objective is the use of informatics in the discovery and management of new knowledge relating to health and disease. This includes the management of the relevant knowledge base. Clinical research informatics could be thought to encompass translational bioinformatics. However, for the present at least, AMIA has chosen to consider it a separate division since the communities of practitioners tend to be separate and since the field is still in its infancy. Strategic Plan, American Medical Informatics Association, 2007 http://www.amia.org/inside/stratplan/ The application of informatics approaches to the clinical- evaluation phase of drug development. These approaches can include clinical- trial simulations to improve trial design and patient selection, as well as electronic capturing and storing of clinical data and protocols. The goal is to reduce expenses and time to market. Clinical trials complexity: Currently there are more than 30 different mathematical descriptions of complexity. However we have yet to understand the mathematical dependency relating the number of genes with organism complexity. JC Venter et. al Sequence of the Human Genome Science 291 (5507): 1347, Feb. 16, 2001 Narrower term: biocomplexity Genomics computational biology: Bioinformatics computational biophysics: Activities of the Theoretical and Computational Biophysics Group center on the structure and function of supramolecular systems in the living cell, and on the development of new algorithms and efficient computing tools for structural biology. The Resource brings the most advanced molecular modeling, bioinformatics, and computational technologies to bear on questions of biomedical relevance. Theoretical and Computational Biophysics Group, Univ. of Illinois Urbana Champaign, About the Group http://www.ks.uiuc.edu/Overview/intro.html Drug discovery informatics data mining: Exploration and analysis, by automatic or semi- automatic means, of large quantities of data in order to discover meaningful patterns or rules. Berry, MJA, Data Mining Techniques for Marketing, Sales and Customer Support John Wiley & Sons, New York 1997 cited in Nature Genetics 21(15): 51- 55 ref 11, 1999 Narrower terms: affinity based data mining, comparative data mining, gene expression database mining, genome database mining influence- based data mining, predictive data mining, proteome database mining, time delay data mining, trends analysis data mining. Increasingly people are talking about text mining (including of the life sciences literature, as well as of sequence and structure databases). Algorithms & data analysis databases: Collections of data in machine- readable form, which can be manipulated by software to appear in varying arrangements and subsets. Databases & software Describes and provides links to around 200 databases and about 30 software tools. Related terms: annotated databases, curated databases, federated databases, integrated databases, non- redundant databases, proprietary databases, redundant databases. Bioinformatics determinism (genetic): Science's review of "The sequence of the human genome" (J. Craig Venter et al 291: 1304-1352 Feb. 16, 2001) concludes that a "paramount challenge awaits: public discussion of this information and its potential for improvement of personal health ... There are two fallacies to be avoided: determinism, the idea that all characteristics of the person are 'hard-wired" by the genome; and reductionism, the view that with complete knowledge of the human genome sequence, it is only a matter of time before our understanding of gene functions and interactions will provide a complete causal description of human variability." Molecular Medicine digital health: The healthcare environment will be profoundly changed by the convergence of technology, and ready access to updated patient information. This meeting will delve into issues of how global connectivity and technology will drive a strategic vision of integrated healthcare delivery systems and cover the use of combinatorial device technology to integrate healthcare systems, and the novel connectivity of global electronic medical record efforts. Clinical management of disease will be addressed through the use of handheld and point-of-care devices. The value of real time patient information to the clinical management team and the pharmaceutical researcher will be leveraged while addressing the ethical and legal implications. Digital Healthcare & Productivity eNewsletter Archive 2005-2008 electronic health records: The healthcare environment will be profoundly changed by the convergence of technology, and ready access to updated patient information. The program will cover the use of combinatorial device technology to integrate healthcare systems, and the novel connectivity of global electronic medical record efforts. Clinical management of disease will be addressed through the use of handheld and point-of-care devices. The value of real time patient information to the clinical management team and the pharmaceutical researcher will be leveraged while addressing the ethical and legal implications. Emerging lessons: Electronic Health Records, Agency for Health Care Research and Quality, http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5562&mode=2&holderDisplayURL=http://prodportallb.ahrq. electronic prescribing: Agency for Healthcare Research & Quality, http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDisplayURL=http://prodportal Clinical informatics Gene OntologyTM GO: The goal of the Gene OntologyTM Consortium is to produce a dynamic controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. http://www.geneontology.org/ Participating Groups include Arabidopsis, C. elegans, Drosophila, Saccharomyces and mouse. Functional genomics Wikipedia http://en.wikipedia.org/wiki/Gene_Ontology genome informatics: The Twelfth International Conference on Genome Informatics (GIW 2001) focuses on Genome Informatics, including, but not limited to, the following areas: genomic database, knowledge extraction from literature, knowledge discovery and data mining from databases, structural genomics, protein structure and function prediction, proteome analysis, pathway analysis, functional genomics, gene expression analysis, gene network analysis, gene structure and function prediction, sequence analysis, motif extraction and search, multiple alignment, phylogenetic tree, linkage analysis program, systems for supporting experimental works (mapping, sequencing, primer design, etc.), high performance computing, simulation of biological system, DNA computing, artificial life, etc. GIW 2001 homepage, Dec. 17-19, 2001, Tokyo, Japan http://giw.ims.u-tokyo.ac.jp/giw2001/ Genomics Good Informatics Practices Guidance Document (GIP): A newly drafted comprehensive body of information of regulatory requirements in the form of existing (GLP, GMP, GCP and Part 11) and currently used standards compiled in one reference guide for an IT system of a life science or healthcare environment. http://www.lsit.org/LSITExecutiveSummaryFinal021111.pdf health IT tools: Agency for Healthcare Research & Quality, http://healthit.ahrq.gov/portal/server.pt?open=512&objID=919&parentname=CommunityPage&parentid=9&mode=2&in_h in silico: In a white paper I wrote for the European Commission in 1988 I advocated the funding of genome programs, and in particular the use of computers. In this endeavour I coined "in silico" following "in vitro" and "in vivo" I think that the first public use of the word is in the following paper: A. Danchin, C. Médigue, O. Gascuel, H. Soldano, A. Hénaut, From data banks to data bases. Res. Microbiol. (1991) 142: 913- 916. You can find a developed account of this story in my book The Delphic Boat, Harvard University Press, 2003, personal communication Antoine Danchin, Institute Pasteur, 2003 Literally "in the computer" (as contrasted with "in vitro" (in glass) or "in vivo" (in life). Can be used to screen out compounds which are not druggable. Drug discovery informatics informatics: The study of the application of computer and statistical techniques to the management of information. In genome projects, informatics includes the development of methods to search databases quickly, to analyse DNA sequence information, and to predict protein sequence and structure from DNA sequence data. ORD Office of Rare Diseases, NIH glossary There is no universally accepted taxonomy for the major domains of informatics today. For purposes of AMIA's education, member service, research programs and policy initiatives, we recognize three domains. These domains include 1. Clinical informatics (including healthcare, research and personal health management) 2. Public health/population informatics 3. Translational bioinformatics Strategic Plan, American Medical Informatics Association, 2007 http://www.amia.org/inside/stratplan/ Narrower terms: bioinformatics; cheminformatics; Computers & computing glossary clinical informatics, molecular informatics, research informatics; Drug discovery & development pharmacoinformatics, pharmainformatics Proteomics protein informatics Information management & interpretation information overload: Biomedicine is in the middle of revolutionary advances. Genome projects, microassay methods like DNA chips, advanced radiation sources for crystallography and other instrumentation, as well as new imaging methods, have exceeded all expectations, and in the process have generated a dramatic information overload that requires new resources for handling, analyzing and interpreting data. Delays in the exploitation of the discoveries will be costly in terms of health benefits for individuals and will adversely affect the economic edge of the country. Opportunities in Molecular Biomedicine in the Era of Teraflop Computing: March 3 & 4, 1999, Rockville, MD, NIH Resource for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana- Champaign "Information overload" is not an overstatement these days. One of the biggest challenges is to deal with the tidal wave of data, filter out extraneous noise and poor quality data, and assimilate and integrate information on a previously unimagined scale. Information management & interpretation information technology: Plays a key role in helping organizations achieve profitable results and keep competitive forces in check. With the completion of the draft sequence of the human genome and the push for protein data analysis, the life sciences industry is faced with the daunting task of creating computing infrastructures that support a high level of data interpretation. Never before has the need for significant computing power been so great. Cambridge Healthtech Institute, IT and Informatics conference series IT Infrastructure interdisciplinary aspects of research: Terminology and ideas relevant to genomics comes from a wide variety of disciplines: analytical chemistry, artificial intelligence, biochemistry, bioinformatics, biomechanics, biophysics, biotechnology, cell biology, clinical and research medicine, computer sciences, developmental and structural biology, electrochemistry, electronics, engineering, enzymology, epidemiology, genetic engineering, imaging, immunology, mathematics, microbiology, molecular biology, optics, pharmacology, public health, statistics, toxicology, virology and aspects of business, chaos theory, ethics and law are all relevant. Few people (if any) can be truly interdisciplinary and expert in all of these subjects. Universities are struggling with the challenge of (and need to) build bridges between departments. Companies are as well. We all need to learn more to participate in informed public debate. Research interoperability: The ability of two or more systems or components to exchange information and to use the information that has been exchanged. Institute of Electrical and Electronics Engineers. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY: 1990 Enabling heterogeneous databases to function in an integrated way, sometimes refers to cross platform functionality and operability across relational, object- oriented, and non- standard types of databases. Information management & interpretation just in time information: About 1,530,000 websites were found with this phrase by Google on Feb 18, 2011 An increasing need as we are deluged with information and data -- and still need time to reflect, discuss and think about what all these means. Information management & interpretation life sciences informatics: Informatics are essential at every step of genomics-based drug discovery and development. The commercial landscape of life sciences information technology has changed dramatically in the last few years. Bioinformatics, in particular, has gone through a dramatic boom/bust. While IT companies are looking to the drug discovery and development arena as a new market opportunity, pharmaceutical companies are faced with rising pressure to reduce (or at least control) costs, and have a growing need for new informatics tools to help manage the influx of data from genomics, and turn that data into tomorrow's drugs. Key IT tools, such as high- performance computing, Web services, and grids, are being used to improve the speed and efficiency of drug discovery and development. True breakthroughs are still lacking, particularly in key areas such as gene prediction, data mining, protein structure modeling and prediction, and modeling of complex biological systems. However, most experts agree that IT and bioinformatics are essential to reaching the improved productivity the pharmaceutical industry craves. Information management & interpretation medical informatics: The field of information science concerned with the analysis and dissemination of medical data through the application of computers to various aspects of health care and medicine. MeSH, 1987 Clinical & Medical informatics metadata: Could elevate the status of the web from machine- readable to something we might call machine- understandable. Metadata is "data about data" or specifically in our current context "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application ("one application's metadata is another application's data"). W3C, "Introduction to RDF Metadata" http://www.w3.org/TR/NOTE-rdf-simple-intro Information about data that enables intelligent, efficient access and management of data. … metadata is always less than the data. Robyne M. Sumpter “White paper on Data Management” Lawrence Livermore National Laboratory, February 10, 1994] http://www.llnl.gov/liv_comp/metadata/papers/whitepaper-draft.html Information management & interpretation molecular modeling: A technique for the investigation of molecular structures and properties using computational chemistry and graphical visualization techniques in order to provide a plausible three- dimensional representation under a given set of circumstances. IUPAC Medicinal Chemistry Drug discovery informatics new paradigms: While many advances are unlikely to be truly new paradigms, a few developments show signs of being more than incremental improvements. Roger Brent compares microarrays to the microscope and telescope because they "enable observation of the previous unobservable" [transcripts expressed under different conditions in cells, tissues, and organisms] R. Brent, "Functional genomics: learning to think about gene expression data" Current Biology 9: R338-R341, May 1999 This is no overstatement. Research nonlinear: Advances in genomic technologies are a mix of incremental improvements to existing technologies (linear) and occasionally, a truly new paradigm or breakthrough. Related terms: disruptive technologies, emerging technologies and complex. Genomics normalization: A knotty area in any measurement process, because it is here that imperfections in equipment and procedures are addressed. The specifics of normalization evolve as a field matures since the process usually gets better, and one’s understanding of the imperfections also gets better. In the microarray field, even larger changes are occurring as robust statistical methods are being adopted. Algorithms & data analysis ontology: From the Greek onto "on being". Metaphysics, nature and essence of existence. Oxford English Dictionary Narrower terms bio- ontology, Gene Ontology TM, molecular biology ontology Ontologies Open
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public health informatics: The systematic application of information and computer sciences to public health practice, research, and learning. It is the discipline that integrates public health with information technology. The development of this field and dissemination of informatics knowledge and expertise to public health professionals is the key to unlocking the potential of information systems to improve the health of the nation. www.nlm.nih.gov/pubs/cbm/phi2001.html MeSH 2003 Molecular Medicine semantic web: The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications. In order to make this vision a reality for the Web, supporting standards, technologies and policies must be designed to enable machines to make more sense of the Web, with the result of making the Web more useful for humans. Facilities and technologies to put machine- understandable data on the Web are rapidly becoming a high priority for many communities. For the Web to scale, programs must be able to share and process data even when these programs have been designed totally independently. The Web can reach its full potential only if it becomes a place where data can be shared and processed by automated tools as well as by people. W3C, Semantic Web Activity Statement, 2001 http://www.w3.org/2001/sw/Activity Ontologies semantics: How the information [in a data file] should be interpreted by others. Russ Altman "Challenges for Biomedical Informatics and Pharmacogenomics, Stanford Medical Informatics, c.2001 http://bmir.stanford.edu/file_asset/index.php/91/BMIR-2001-0898.pdf Related terms: controlled vocabularies, ontologies, semantic web, taxonomies Information management & interpretation social informatics: A serviceable working conception of "social informatics" is that it identifies a body of research that examines the social aspects of computerization. A more formal definition is "the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their interaction with institutional and cultural contexts." ... diversity of communication outlets and specialized terminologies makes it hard for many non- specialists (and even specialists) to locate important studies. Rob Kling, What is social informatics and why does it matter? D-Lib 5(1): Jan. 1999 http://www.dlib.org/dlib/january99/kling/01kling.html Information management & interpretation standards: Bioinformatics Bio-ontology Standards Group, Data Model Standards Group; Microarrays data analysis, standards taxonomies: Frustrations with search engines and information retrieval (and information overload) have led to increased interest in specialized taxonomies. A form of controlled vocabulary, with hierarchical relationships (broader terms, narrower terms) which provide additional suggestions for browsing, as do lateral relationships (related terms) and preferred terms. While there is theoretical interest in natural language processing, a very small percentage of web search engine queries actually use natural language processing successfully. See also FAQ question #4 for more about taxonomies. Information management & interpretation text mining: Usually, "text mining" is used to indicate a text search technique. But, we think of text mining as having more functions. Text mining technologies extract more information than just picking up keywords from texts: facts, author's intentions, their expectations, and their claims. This knowledge is helpful to many applied tasks such as marketing, trend analysis, claim processing, generating FAQ, and so on. Text Mining, TRL, IBM, 2000 http://www.trl.ibm.com/projects/s7710/tm/index_e.htm Competition in the pharmaceutical industry has increasingly become based upon better recognition and analysis of information, much of which is available as published text. Information management & interpretation top-down: A systems approach, which looks at the big picture and complexity. Genomics is essentially a top- down approach, the opposite of a bottom- up approach. Our ways of thinking have been so profoundly influenced by bottom- up, reductionist approaches that we are having to learn to think in very different ways to begin to fully explore. XML eXtensible Markup Language : The universal format for structured documents and data on the Web W3C, "Extensible Markup Language (XML)" 2002 http://www.w3.org/XML/ IT Infrastructure Bibliography
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