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You are here Biopharmaceutical/ Genomic Glossaries homepage > Informatics for pharmaceuticals
Informatics Overview <%end if%> SCOPE NOTE Informatics
includes hardware, software, artificial
intelligence, data science, machine learning, NLP Natural Language
Processing, cloud computing, data integration, data security, data
privacy, IOT Internet of Things, bioinformatics, cheminformatics clinical
informatics, health informatics including electronic medical records,
telemedicine, and wearables
Related glossaries include Algorithms & data analysis Bioinformatics Cheminformatics Clinical & Medical informatics Drug discovery informatics Information management & interpretation IT Infrastructure Research Informatics term index 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
BioIT
World
April 21-23, 2020 • Boston, MA Program | Next
Gen Technology, Big Data, Personalized Medicine 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 cheminformatics: 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: 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 informatics 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 data scientist: a high-ranking professional with the training and curiosity to make discoveries in the world of big data. The title has been around for only a few years. (It was coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and FaceBook.) … More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set.. … As they make discoveries, they communicate what they’ve learned and suggest its implications for new business directions. Often they are creative in displaying information visually and making the patterns they find clear and compelling. … Data scientists’ most basic, universal skill is the ability to write code. This may be less true in five years’ time, when many more people will have the title “data scientist” on their business cards. More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both. … Data scientists want to be in the thick of a developing situation, with real-time awareness of the evolving set of choices it presents. Data Scientist: The Sexiest Job of the 21st Century Thomas H. Davenport and D.J. Patil, Harvard Business Review Oct 2012 http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/5 Data science & machine learning 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
deep
learning: –
another hot topic buzzword – is simply machine learning which is derived
from “deep” neural nets. These are built by layering many networks on top
of each other, passing information down through a tangled web of
algorithms to enable a more complex simulation of human learning. Due to
the increasing power and falling price of computer processors, machines
with enough grunt to run these networks are becoming increasingly
affordable. What is Machine Learning: A complete beginner’s guide in 2017,
Bernard Marr, Forbes 2017 May 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 broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine. FDA Digital Health https://www.fda.gov/medicaldevices/digitalhealth/ Digital health is not a new concept. Seth Frank, writing 16 years ago, penned “Digital Health Care—The convergence of health care and the Internet” in The Journal of Ambulatory Care Management. Today, technology is rapidly transforming healthcare. Eric Topol’s The Creative Destruction of Medicine enumerates how these digital technologies, social networking, mobile connectivity and bandwidth, increasing computing power and the data universe will converge with wireless sensors, genomics, imaging, and health information systems to creatively destroy medicine as we know it. He refers to this as digital medicine, or the digitization of human beings. What Digital Health is (and Isn’t) 2013: Malay Gandhi. Updated 2017: Bill Evans. Rock Health https://rockhealth.com/what-digital-health-is-and-isnt/
electronic
health records:
Electronic
Health Records (EHRs) are safe and confidential records that your doctor,
other health care provider, medical office staff, or a hospital keeps on a
computer about your health care or treatments. If your providers use
electronic health records, they can join a network to securely share your
records with each other. EHRs can help lower the chances of medical
errors, eliminate duplicate tests, and may improve your overall quality of
care. EHRs can help your providers have the same up-to-date information
about your conditions, treatments, tests, and prescriptions. Electronic
Health Records, Medicare.gov http://www.medicare.gov/manage-your-health/electronic-health-records/electronic-health-records.html 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. 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. Ontologies Wikipedia http://en.wikipedia.org/wiki/Gene_Ontology genome informatics: Genome informatics is the field in which computer and statistical techniques are applied to derive biological information from genome sequences. Genome informatics includes methods to analyse DNA sequence information and to predict protein sequence and structure. Nature latest research and news https://www.nature.com/subjects/genome-informatics Genomic Informatics
Good
Informatics Practices
Guidance
Document (GIP):
The
GIP Guidance is a comprehensive resource for implementing a trusted IT
ecosystem for ensuring the reliability and effective use of critical
health data. The Guidance includes regulatory requirements (e.g., GLP,
GMP, GCP and Part 11), standards, best practices and case studies.
http://www.himss.org/ResourceLibrary/Content.aspx?ItemNumber=13191 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:
informatics
is a branch of information engineering. It
involves the practice of information
processing and
the engineering of information
systems,
and as an academic
field it
is an applied form
of information
science.
The field considers the interaction between humans and information alongside the
construction of interfaces, organisations, technologies and systems. As such,
the field of informatics has great breadth and encompasses many subspecialties,
including disciplines of computer
science, information
systems, information
technology and statistics.
Since the advent of computers, individuals and organizations increasingly
process information digitally. This has led to the study of informatics with
computational, mathematical, biological, cognitive and social aspects, including
study of the social impact of information technologies.
https://en.wikipedia.org/wiki/Informatics
accessed 2017 Oct 27 Narrower terms: bioinformatics; cheminformatics; clinical informatics: molecular informatics, research informatics; Drug discovery & development: pharmainformatics Protein informatics 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 Molecular Biomedicine in the Era of Teraflop Computing - DDDAS.org "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: the use of computers to store, retrieve, transmit, and manipulate data,[1] or information, often in the context of a business or other enterprise.[2] IT is considered to be a subset of information and communications technology (ICT). An information technology system (IT system) is generally an information system, a communications system or, more specifically speaking, a computer system – including all hardware, software and peripheral equipment – operated by a limited group of users. ...The term is commonly used as a synonym for computers and computer networks, but it also encompasses other information distribution technologies such as television and telephones. Several products or services within an economy are associated with information technology, including computer hardware, software, electronics, semiconductors, internet, telecom equipment, and e-commerce.[5][a]Wikipedia accessed 2019 July 18 https://en.wikipedia.org/wiki/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. 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, increasing to 259,000 by March 2019. 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 Linked Data Glossary http://www.w3.org/TR/ld-glossary/ 2013 machine learning: At
its most simple, machine learning is about teaching computers to learn in
the same way we do, by interpreting data from the world around us,
classifying it and learning from its successes and failures. In fact,
machine learning is a subset, or better, the leading
edge of artificial intelligence. How
did machine learning come about? Building
algorithms capable of doing this, using the binary “yes” and “no” logic of
computers, is the foundation of machine learning – a phrase which was
probably first used during serious research by Arthur Samuel at IBM during
the 1950s. Samuel’s earliest experiments involved teaching machines to
learn to play checkers. … For
example, in medicine, machine learning is being applied to genomic data to
help doctors understand, and predict, how
cancer spreads,
meaning more effective treatments can be developed. What is Machine
Learning: A complete beginner’s guide in 2017, Bernard Marr, Forbes 2017
May https://www.forbes.com/sites/bernardmarr/2017/05/04/what-is-machine-learning-a-complete-beginners-guide-in-2017/#33c58c2f578f 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 Data Science & Machine learning 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
predictive
analytics:
encompasses a variety of statistical techniques from modeling, machine
learning, and data
mining that
analyze current and historical facts to make predictions about
future, or otherwise unknown, events. .. The core of predictive analytics
relies on capturing relationships between
explanatory variables and
the predicted variables from past occurrences, and exploiting them to
predict the unknown outcome. It is important to note, however, that the
accuracy and usability of results will depend greatly on the level of data
analysis and the quality of assumptions.
Wikipedia accessed April 12 2015
http://en.wikipedia.org/wiki/Predictive_analytics
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 Ontologies 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. Ontologies && taxonomies
text mining:
Text mining is the process of analyzing
collections of textual materials in order to capture key concepts and
themes and uncover hidden relationships and trends without requiring that
you know the precise words or terms that authors have used to express
those concepts. Although they are quite different, text mining is
sometimes confused with information retrieval. While the accurate
retrieval and storage of information is an enormous challenge, the
extraction and management of quality content, terminology, and
relationships contained within the information are crucial and critical
processes.
https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.ta.help/tm_intro_tm_defined.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)" 2016 http://www.w3.org/XML/ Ontologies Taxonomies
Informatics Resources |
