With changes in sequencing technology and methods, the rate of acquisition of human and other genome data over the next few years will be ~100 times higher than originally anticipated. Assembling and interpreting these data will require new and emerging levels of coordination and collaboration in the genome research community to develop the necessary computing algorithms, data management and visualization system.  Lawrence Berkeley Lab, US "Advanced Computational Structural Genomics"

Finding guide to terms in these glossaries  Informatics  Map   Site Map   The dividing line between this glossary and Information management& interpretation is fuzzy - in general Algorithms & data analysis focuses on structured data, while Information management & interpretation centers on unstructured data.

Other related glossaries include  
Applications: Drug Discovery & Development   Proteomics  
Informatics: Bioinformatics   Chemoinformatics   Computers & computing   In silico & molecular Modeling   Ontologies   Research
Technologies: Microarrays & protein chips   Sequencing
Biology: Protein Structures   Sequences, DNA & beyond.  

3D-QSAR Three-Dimensional Quantitative Structure-Activity Relationships: In silico & molecular modeling glossary

adaptive clinical trials: Clinical trials and drug approvals

ANOVA Analysis Of Variance: Error model based on a standard statistical approach. a generalization of the familiar t-test that allows multiple effects to be compared simultaneously, in contrast to the t-test. An ANOVA model is expressed as a large set of equations that can be solved, given a dataset of measurements, using standard software. 

affinity based data mining: Large and complex data sets are analyzed across multiple dimensions, and the data mining system identifies data points or sets that tend to be grouped together.  These systems differentiate themselves by providing hierarchies of associations and showing any underlying logical conditions or rules that account for the specific groupings of data.  This approach is particularly useful in biological motif analysis. "Data mining" Nature Biotechnology 18: 237-238 Supp. Oct. 2000  

Broader term: data mining 

agglomerative method: See under cluster analysis

algorithm:  A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. MeSH, 1987

Algorithms fuel the scientific advances in the life sciences. They are required for dealing with the large amounts of data produced in sequencing projects, genomics or proteomics. Moreover, they are crucial ingredients in making new experimental approaches feasible... Algorithm development for Bioinformatics applications combines Mathematics, Statistics, Computer Science as well as Software Engineering to address the pressing issues of today's biotechnology and build a sound foundation for tomorrow's advances.  Algorithmics Group, Max Planck Institute for Molecular Genetics, Germany http://algorithmics.molgen.mpg.de/

Rules or a process, particularly in computer science. In medicine a step by step process for reaching a diagnosis or ruling out specific diseases.  May be expressed as a flow chart in either sense. Greater efficiencies in algorithms, as well as improvements in computer hardware have led to advances in computational biology. A computable set of steps to achieve a desired result.

From the Persian author Abu Ja'far Mohammed ibn Mûsâ al-Khowârizmî who wrote a book with arithmetic rules dating from about 825 A.D. [NIST]

Narrower terms: docking algorithms, sequencing algorithms, genetic algorithm, heuristic algorithm.  Related terms heuristic, parsing; Sequencing dynamic programming methods.

artificial intelligence (AI): A wide- ranging term encompassing computer applications that have the ability to make decisions; the ability to explain reasoning is evidence of intelligence.  Also covers methods that have the ability to learn. [J Glassey et al. “Issues in the development of an industrial bioprocess advisory system” Trends in Biotechnology 18 (4):136-41 April 2000] 

Or as some people have noted, laboriously trying to get computers to do what people do intuitively, without great effort. Conversely there are things computer can do (relatively) effortlessly such as massive numbers of  error- free calculations. The most promising applications seem to involve incorporating both computer aided consideration of many possibilities, combined with human judgment.  

Narrower terms: cellular automata, expert systems, fuzzy logic, genetic algorithms, neural nets Related term: training sets. 

American Association of Artificial Intelligence: Topics  http://www.aaai.org/AITopics/html/current.htmlz
How to do research in the MIT AI Lab, a whole bunch of current, former, and honorary MIT AI Lab graduate students, 1988-1997? http://www.cs.indiana.edu/mit.research.how.to/mit.research.how.to.html

Virtual Library Artificial Intelligence, Jonathan Bowen, South Bank Univ. UK, 2002 http://www.afm.sbu.ac.uk/ai/   University and government research sites, newsgroups, commercial sites and products, programming languages, journals, bibliographies, “interactive things” and other information

artificial neural nets: Algorithms simulating the functioning of human neurons and may be used for pattern recognition problems, e.g., to establish quantitative structure- activity relationships. [IUPAC Computational]

Broader term neural nets; Related terms: Drug discovery and development drug design

Bayesian clinical trials: Drug approvals

Bayesian network modeling:  This report describes a powerful and novel predictive tool called Bayesian network modeling and demonstrates its application in clinical forecasting.  Insight Pharma Reports, Bayesian Forecasting of Phase III Outcomes: The Next Wave in Predictive Tools,  2007

Bayesian network: Wikipedia  http://en.wikipedia.org/wiki/Bayesian_network 
Bayesian networks:  A quick intro, Karen Sachs, Biomedical Computation Review, Summer 2005 http://www.biomedicalcomputationreview.org/1/1/9.pdf A computational analysis approach, machine learning tool. 

Bayesian statistics: The fundamental idea in Bayesian statistics is that one’s uncertainty about an unknown quantity of interest is represented by probabilities for possible values of that quantity.... The Bayesian paradigm states that probability is the only measure of one’s uncertainty about an unknown quantity. In a Bayesian clinical trial, uncertainty about an endpoint (also called parameter) is quantified according to probabilities, which are updated as information is gathered from the trial.  Center for Devices & Radiological Health, FDA, Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials - Draft Guidance for Industry and FDA Staff , This guidance document is being distributed for comments purposes only. Draft released for comment on May 23, 2006 docket number 2006D-0191. http://www.fda.gov/cdrh/osb/guidance/1601.html#4 

biomathematics: The application of mathematics to problems in biology and medicine. An essential tool in fields such as population genetics, cellular neurobiology, comparative genetics, biomedical imaging, pharmacokinetics, and epidemiology. It plays an increasingly vital role in the effort to understand diseases and disorders, and to improve therapies.  Collection Development Manual, National Library of Medicine, US 2004  http://www.nlm.nih.gov/tsd/acquisitions/cdm/subjects14.html 

Related terms: bioinformatics, computational biology, molecular modeling

biometrics: The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Biometric technology is a way to achieve fast, user- friendly authentication with a high level of accuracy. [Biometrics Consortium]  http://www.biometrics.org/REPORTS/CTSTG96/

Bonferroni correction: A multiple test correction method. To address false positives through this method, you can simply divide your desired false- positive rate by the number of tests, and use that modified number to declare any single change to be significant. The Bonferroni correction is extremely conservative, and many statisticians argue against its use. 

bootstrapping: Kerr and Churchill use a bootstrapping procedure to calculate confidence intervals for the fitted values. Any bootstrapping procedure works by perturbing the original dataset and re-solving the model many times, often thousands of times. [Similar methods are sometimes called resampling or jackknife methods.] This generates a large number of values for each variable (one for each perturbed dataset), and one then estimates the true values of the variable, confidence intervals, and so on, from these values. The tricky part of the procedure is deciding how to perturb the dataset. 

chaos theory and biology:  Skinner JE et al, Application of chaos theory to biology and medicine, Integr Physiol Behav Sci, 1992 Jan-Mar; 27 (1): 39- 53 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1576087&dopt=Abstract  

Chaos Hypertextbook, Glen Elert, 1995- 2002 http://hypertextbook.com/chaos/

cluster analysis: The clustering, or grouping, of  large data sets (e.g., chemical and/ or pharmacological data sets) on the basis of similarity criteria for appropriately scaled  variables that represent the data of interest. Similarity criteria (distance based, associative, correlative, probabilistic) among the several clusters facilitate the recognition of patterns and reveal otherwise hidden structures (Rouvray, 1990; Willett, 1987, 1991). [IUPAC Computational]

A set of statistical methods used to group variables or observations into strongly inter- related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health- related phenomenon with well- defined distribution patterns in relation to time or place or both. MeSH, 1990

Has been used in medicine to create taxonomies of diseases and diagnosis and in archaeology to establish taxonomies of stone tools and funereal objects. Cluster analysis can be supervised, unsupervised or partially supervised

Related terms: clustering analysis, dendogram, heat map, pattern recognition, profile chart. Narrower terms: hierarchical clustering, k-means clustering, self- organizing maps

ClogP values: In silico & molecular modeling glossary

clustering analysis: This is a general type of analysis that involves grouping gene or array expression profiles based on similarity. Clustering is a major subfield within the broad world of numerical analysis, and many specific clustering methods are known. 

coefficient of variation (CV): The standard deviation of a set of measurements divided by their mean.

common factor analysis: See under principle component analysis PCA

comparative data mining: Focuses on overlaying large and complex data sets that are similar to each other ...particularly useful in all forms of clinical trial meta  analyses ... Here the emphasis is on finding dissimilarities, not similarities. "Data mining" Nature Biotechnology Vol. 18: 237-238 Supp Oct.. 2000 

Broader term: data mining

Comparative Molecular Field Analysis CoMFA: In silico & molecular modeling glossary

curse of dimensionality: (Bellman 1961) refers to the exponential growth of hypervolume as a function of dimensionality. In the field of NNs [neural nets], curse of dimensionality expresses itself in two related problems.  Janne Sinkkonen "What is the curse of dimensionality?" Artificial Intelligence FAQ, at comp-ai-neuralnets.org http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-13.html

Related term: high-dimensionality

data cleaning: Removal and/or correction of erroneous data introduced by data entry errors, expired validity of data, or by some other means. Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary

The quality of data in sequence databases is highly variable.  This is receiving increasing attention.  Ensembl Bioinformatics glossary differentiates data of varying quality. 

Related terms: data cleansing, data scrubbing

Data integrity and cleansing tools, DMOZ, 2003 * http://dmoz.org/Computers/Software/Databases/Data_Warehousing/Data_Integrity...

data credibility: Different labs have different reputations, and scientists look at work produced by their peers in a subjective light. This data credibility issue creates a need to tag almost every data item with a confidence factor. This is so that, as you create your next experimental hypothesis, you know that the quality of the information you are relying upon is high enough that you can go profitably down the scientific line of inquiry that you are pursuing. The Life Science Industry Represents Unique Opportunities for Informatics Companies: An Interview with Shiv Tasker of Blackstone Computing, CHI's GenomeLink 25.1 http://www.healthtech.com/newsarticles/issue25_1.asp

data integration: The term "data integration" is used generically within the industry for describing disparate situations. Consequently, considerable confusion results regarding the best practices for solving specific, data integration problems. There are a number of markedly different approaches to data integration, each with its own strengths and weaknesses, and many different technologies are available for each approach. All data integration efforts are initiated to support particular research objectives. Although they are aimed toward the same strategic goal, they can differ substantially in the specific problems that they are trying to solve, in the scale of the integration, and in the types of data that are integrated. The strategies and technologies that best apply to address specific objectives are unlikely to be the same.  Key Trends Influencing Informatics Initiatives in Life Science Companies: An Interview with Eric Meyers and Jack Pollard of 3rd Millennium, CHI's GenomeLink 29.2 http://www.healthtech.com/newsarticles/issue29_2.asp

Related terms: data mining - integrating, data reduction methods; Information management & interpretation glossary interoperability; Computers XML; Omes & -omics glossary integromics: 

data management: Each new generation of DNA sequencers, mass spectrometers, microscopes, and other lab equipment produces a richer, more detailed set of data. We’re already way beyond gigabytes (GB): a single next-generation sequencing experiment can produce terabytes (TB) of data in a single run. As a result, any organization running hundreds of routine experiments a month or year, or trying to handle the output of next-generation sequence instruments, quickly finds itself with a massive data management problem.  Data Management: The Next Generation, Salvatore Salamone, BioIT World, Oct 2007      http://www.bio-itworld.com/issues/2007/oct/cover-story-data-management

SEE also algorithms, artificial intelligence, data cleaning, data mining, data reduction methods, expert systems, factorial design, fuzzy logic, knowledge based systems, neural networks, normalization, parsing, pattern recognition, SPC Structure- Property Correlations, visualization and various statistical methods. CoMFA, decision tress, factorial design, mosaic plots, multivariate statistics, Partial Least Squares PLS, Principal Components Analysis PCA, recursive partitioning; Clinical trials glossary meta-analysis; Information management & interpretation glossary

data mart:  A department specific data warehouse. There are two types of data marts - independent and dependent. An independent data mart is fed data directly from the legacy environment. A dependent data mart is fed data from the enterprise data warehouse. In the long run, dependent data marts are architecturally much more stable than independent data marts. Bill Inmon, Glossary of Data Warehousing, 2002-2005  http://www.inmoncif.com/library/glossary/  

data mining: The biopharmaceutical industry is grappling not only with sheer data volume but with the ability of researchers to extract information through identification and contextual analysis of those data that are relevant to a particular set of investigations. Data Mining in Drug Development and Translational Medicine July 2009 Table of Contents | Tables and Figures | Executive Summary

Nontrivial extraction of implicit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases.  W. Frawley and G. Giatetsky-Schapiro and C. Matheus, “Knowledge Discovery in Databases: An Overview.” AI Magazine,  213- 228, Fall 1992

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

May need to incorporate related techniques such as cluster analysis or visualization.

Narrower terms: affinity based data mining, comparative data mining, data mining - integrated, data mining- structure based, influence-based data mining, predictive data mining, text mining, time delay data mining, trends analysis data mining.  Molecular Imaging image data mining. Related terms: data warehouse

BioMedCentral for data mining  http://www.biomedcentral.com/info/about/datamining/
Data Mine http://www.the-data-mine.com/
KDNuggets http://www.kdnuggets.com/
SIGKDD, Special Interest Group Knowledge Discovery in Data and Data Mining, Association for Computing Machinery  http://www.acm.org/sigkdd/

data reduction methods: Includes cluster analysis, currently the best known data reduction method in the microarray field.  

Related terms: data cleaning, data cleansing, data scrubbing

data scrubbing:  Correcting, completing, verifying and deduping data, particularly for data warehouses.

Data scrubbing, Tommy Peterson, ComputerWorld, Feb. 10, 2003 http://www.computerworld.com/databasetopics/data/story/0,10801,78230,00.html
What is.com definition, 2003 http://searchdatabase.techtarget.com/sDefinition/0,,sid13_gci880972,00.html

Related terms: data cleaning, data cleansing, data reduction methods

data sharing: Research glossary

data visualization: Information management & interpretation glossary

data warehouse: An integrated repository of data from multiple, possibly heterogeneous data sources, presented with consistent and coherent semantics. Warehouses usually contain summary information represented on a centralized storage facility. Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary

A collection of integrated subject oriented data bases designed to support the DSS function, where each unit of data is relevant to some moment in time. The data warehouse contains atomic data and lightly summarized data. A data warehouse is a subject oriented, integrated, non volatile, time variant collection of data designed to support management DSS needs.  Inmon, Bill, Glossary of Data Warehousing, 2002-2005  http://www.inmoncif.com/library/glossary/  

Related terms: data mining, global schema

database mining: See data mining

databases: Bioinformatics glossary   Databases & software directory

decision trees: Hierarchical series of questions leading to specific action steps -- to guide manufacturers and reviewers in determining the level and extent of safety testing needed at various stages. Report Recommends More Explicit Guidelines For Assessing Safety of New Ingredients Added to Infant Formula, National Academy of Sciences press release, 2004  http://www4.nationalacademies.org/news.nsf/isbn/0309091500?OpenDocument 

dendogram: A tree diagram that depicts the results of hierarchical clustering. Often the branches of the tree are drawn with lengths that are proportional to the distance between the profiles or clusters. Dendograms are often combined with heat maps, which can give a clear visual representation of how well the clustering has worked. 

Related terms: cluster analysis, heat maps, profile charts

distance functions or similarity scores: The key issue in comparing expression profiles is deciding what it means for two profiles to be "similar." Mathematically, we need a function that takes two expression profiles and calculates a similarity score. It is sometimes easier to work with the opposite concept of distance, and people often speak of distance functions instead of similarity scores. Many similarity or distance functions are used in microarray work, and there is no consensus as to which one is best. 

Narrower terms: Euclidean distance, Pearson correlation

divisive method: See under cluster analysis

error model: A mathematical formulation that identifies the sources of error in an experiment. An error model provides a mathematical means of compensating for the errors in the hope that this will lead to more accurate estimates of the true expression levels and also provides a means of estimating the uncertainty in the answers. An error model is generally an approximation of the real situation and embodies numerous assumptions; therefore, its utility depends on how good these assumptions are. The model can be expressed as a set of equations, as an algorithm, or using any other mathematical formalisms. ... The term error model has become very popular among software providers, particularly in light of the success of Rosetta’s Resolver, which incorporates an error model. As a result, some software developers may use the term inappropriately. Not everything that is called an error model really is one. 

Euclidean distance: Commonly used distance function, which works by treating each expression profile as defining a point in a multidimensional space. 

evolutionary computation: Encompasses methods of simulating on a computer. The term is relatively new and represents an effort bring together researchers who have been working in closely related fields but following different paradigms. The field is now seen as including research in Strategies, Evolutionary PROGRAMMING, ARTIFICIAL LIFE, and so forth. For a good overview see the editorial introduction to Vol. 1, No. 1 of "Evolutionary Computation" (MIT Press, 1993). That, along with the papers in the issue, should give you a good idea of representative research   Evolutionary computing glossary, Hitch Hiker's Guide to Evolutionary Computation Issue 8.1, released 29 March 2000

expert systems:  A computer-based program that encodes rules obtained from process experts usually in the form of  “if - then” statements. J Glassey et al. “Issues in the development of an industrial bioprocess advisory system” Trends in Biotechnology 18 (4):136-41 April 2000

Related term: artificial intelligence.

factorial design FD: An experimental design technique in which each variable (factor or  descriptor) is investigated at fixed levels. In a two- level FD, each variable can take two values, e.g., high and low lipophilicity.  [IUPAC Computational]

fuzzy: In contrast to binary (true/ false) terms allows for looser boundaries for sets or concepts.

fuzzy logic: A superset of conventional (Boolean) logic that has been extended to handle the concept of  partial truth- truth values between “completely true” and ‘completely false”.  Introduced by Dr. Lotfi  Zadeh (Univ. of California - Berkeley) in the 1960’s as a means to model the uncertainty of natural language. AI FAQ, Carnegie Mellon University Computer Science Department http://www.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq-doc-2.html

Approximate, quantitative reasoning that is concerned with the linguistic ambiguity which exists in natural or synthetic language. At its core are variables such as good, bad, and young as well as modifiers such as more, less, and very. These ordinary terms represent fuzzy sets in a particular problem. Fuzzy logic plays a key role in many medical expert systems. MeSH, 1993

genetic algorithm GA:  Method for library design by evaluating the fit of a parent library to some desired property (e.g. the level of activity in a biological assay, or the computationally determined diversity of the compound set) as measured by a fitness function. The design of more optimal daughter libraries is then carried out by a heuristic process with similarities to genetic selection in that it employs replication, mutation, deletions etc. over a number of generations. [IUPAC Combinatorial Chemistry]

An optimization algorithm based on the mechanisms of Darwinian evolution which uses random mutation, crossover and selection procedures to breed better models or solutions from an originally random starting population or sample. (Rogers and Hopfinger, 1994). IUPAC Computational

Related terms: evolutionary computation ; Drug discovery & development drug design. Narrower term: genetic programming 

genetic programming:  A subset of genetic algorithms. The members of the populations are the parse trees of computer programs whose fitness is evaluated by running them. The reproduction operators (e.g. crossover) are refined to ensure that the child is syntactically correct (some protection may be given against semantic errors too). This is achieved by acting upon subtrees. Genetic programming is most easily implemented where the computer language is tree structured so there is no need to explicitly evaluated its parse tree. This is one of the reasons why Lisp is often used for genetic programming. This is the common usage of the term genetic programming however it has also been used to refer to the programming of cellular automata and neural networks using a genetic algorithm. William Langdon "Genetic programming and data structures glossary" UK, 1997 

Genetic Programming Organization http://www.genetic-programming.org
International Society for Genetic and Evolutionary Computation http://www-illigal.ge.uiuc.edu:8080/

genome mining: In an initial data- mining effort, the draft human genome was searched to find paralogs of known tumor suppressor genes, and for gene arrangements, which are typical of oncogenes, in cancer cells. The results were disappointing, indicating that although knowledge of the human genome will undoubtedly be of great help, other approaches to identify new oncogenes are needed. TG Boyer et. al. "Genome mining for human cancer genes: wherefore art thou?" Trends in Molecular Med 189, May 2001 

genomic data: Genomics glossary

global schema: A schema, or a map of the data content of a data warehouse that integrates the schemata from several source repositories. It is "global", because it is presented to warehouse users as the schema that they can query against to find and relate information from any of the sources, or from the aggregate information in the warehouse. Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary] 

Broader term: schema 

Hansch analysis: The investigation of the quantitative relationship between the biological activity of a series of compounds and their physicochemical substituent or global parameters representing hydrophobic, electronic, steric and other effects using multiple regression correlation methodology. [IUPAC Medicinal Chemistry]

Related term: QSAR

heat map:  A rectangular display that is a direct translation of a Cluster- format data table. Each cell of the data table is represented as a small color- coded square in which the color indicates the expression value. Generally green indicates low values, black medium values, and red high ones, although this is user- settable. The net effect is a colored picture in which regions of similar color indicate similar profiles or parts of profiles.

Related terms: cluster analysis, dendogram, heat map, profile chart; Expression glossary

heuristic: Tools such as genetic algorithms or neural networks employ heuristic methods to derive solutions which may be based on purely empirical information and which have no explicit rationalization. [IUPAC Combinatorial Chemistry] 

Trial and error methods.  

Narrower terms: heuristic algorithm, metaheuristics

heuristic algorithm:  A programming strategy for solving computationally resistant problems that utilizes self- educating techniques (i.e., feedback evaluation) to improve performance (e.g., FASTA). Problem solving by such experimental,  trial- and- error methods does not guarantee the optimal solution. [labvelocity.com]

Hidden Markov Models HMM: In silico & molecular modeling glossary

Related term: simulated annealing

hierarchical clustering: Unsupervised clustering approach used to determine patterns in gene expression data. Output is a tree- like structure. 

Related term: cluster analysis, self- organizing maps

high-dimensionality:  Many applications of machine learning methods in domains such as information retrieval, natural language processing, molecular biology, neuroscience, and economics have to be able to deal with various sorts of discrete data that is typically of very high dimensionality.

One standard approach to deal with high dimensional data is to perform a dimension reduction and map the data to some lower dimensional representation. Reducing the data dimensionality is often a valuable analysis by itself, but it might also serve as a pre- processing step to improve or accelerate subsequent stages such as classification or regression. Two closely related methods that are often used in this context and that can be found in virtually every textbook on unsupervised learning are principal component analysis (PCA) and factor analysis. Thomas Hoffmann, Brown Univ.  Statistical Learning in High Dimensions, Breckenridge CO, Dec. 1999 http://www-2.cs.cmu.edu/~mmp/workshop-nips99/speakers.html

See also under learning algorithms; Related terms: cluster analysis, curse of dimensionality, dimensionality reduction, ill- posed problem, neural nets, principal components analysis

ill-posed problems: In the 1960s [Russian mathematician Andrei Nikolaevich] Tikhonov began to produce an important series of papers on ill- posed problems. He defined a class of regularisable ill- posed problems and introduced the concept of a regularising operator which was used in the solution of these problems. Combining his computing skills with solving problems of this type Tikhonov gave computer implementations of algorithms to compute the operators which he used in the solution of these problems..  "Andrei Nikolaevich Tikhonov", MacTutor History of Mathematics, Univ. of St. Andrews, Scotland, 1999 

Problems without a unique solution, problems without any solution.  Life sciences data tends to be very noisy, leading to ill-posed problems. Interpretation of microarray gene expression data is an ill- posed problem. 

Compare well- posed problem

influence based data mining: Complex and granular (as opposed to linear) data in large databases are scanned for influences between specific data sets, and this is done along many dimensions and in multi- table formats.  These systems find applications wherever there are significant cause and effect relationships between data sets - as occurs, for example in large and multivariant gene expression studies, which are behind areas such as pharmacogenomics. "Data mining" Nature Biotechnology Vol. 18: 237- 238 Supp. Oct. 2000

Broader term: data mining

informatics: Information management & interpretation glossary

information management: Information management & interpretation glossary

information theory: Founded by Claude Shannon in the 1940's, has had an enormous impact on communications engineering and computer sciences. 

Shannon's work, Bell Labs  http://cm.bell-labs.com/cm/ms/what/shannonday/work.html

Information theory primer, Tom Schneider, National Cancer Institute, US, 2002 http://www.lecb.ncifcrf.gov/~toms/paper/primer/

jackknife: See under bootstrapping

k-means clustering: The researcher picks a value for k, say k = 10, and the algorithm divides the data into that many clusters in such a way that the profiles within each cluster are more similar than those across clusters. The actual algorithms for this are quite sophisticated. Although the core algorithms require that a value of k be selected up front, methods exist that adaptively select good values for k by running the core algorithm several times with different values.  A non-hierarchical method.  

Broader terms: cluster analysis, neural nets

knowledge based systems: An extension of the expert system concept wherein additional forms of knowledge, such as mathematical models, are incorporated with the expert rules. J Glassey et al. “Issues in the development of an industrial bioprocess advisory system” Trends in Biotechnology 18 (4):136- 141 April 2000

Related term: data mining.   

Knowledge Discovery in Databases (KDD):  The notion of Knowledge Discovery in Databases (KDD) has been given various names, including data mining, knowledge extraction, data pattern processing, data archaeology, information harvesting, siftware, and even (when done poorly) data dredging. Whatever the name, the essence of KDD is the "nontrivial extraction of implicit, previously unknown, and potentially useful information from data" (Frawley et al 1992). KDD encompasses a number of different technical approaches, such as clustering, data summarization, learning classification rules, finding dependency networks, analyzing changes, and detecting anomalies (see Matheus et al 1993). Gregory Piatetsky- Shapiro, KDD Nuggets FAQ, KDD Nuggets News, 1994 http://www.kdnuggets.com/news/94/n6.txt

Google = about 241,000 July 19, 2002; about 321,000 Oct 22, 2007

Related term: data mining

knowledge management: Information management & interpretation glossary

lexical parsing: See under parsing

machine learning:   Wikipedia  http://en.wikipedia.org/wiki/Machine_learning 

In Knowledge Discovery, machine learning is most commonly used to mean the application of induction algorithms, which is one step in the knowledge discovery process. This is similar to the definition of empirical learning or inductive learning in Readings in Machine Learning by Shavlik and Dietterich. Note that in their definition, training examples are ``externally supplied,'' whereas here they are assumed to be supplied by a previous stage of the knowledge discovery process. Machine Learning is the field of scientific study that concentrates on induction algorithms and on other algorithms that can be said to ``learn.'' Glossary of terms, Ron Kohavi, Machine Learning, 30, 271- 274, 1998 

Related term: supervised, training sets

AAAI Machine Learning, http://www.aaai.org/AITopics/html/machine.html American Association for Artificial Intelligence 

MathML: Intended to facilitate the use and re-use of mathematical and scientific content on the Web, and for other applications such as computer algebra systems, print typesetting, and voice synthesis. W3C http://www.w3.org/Math/whatIsMathML.html

medical informatics: Molecular Medicine glossary

metadata: Information management & interpretation glossary

metaheuristics:  Widely used to solve important practical combinatorial optimization problems. However, due to the variety of techniques and concepts comprised by metaheuristics, there is still no commonly agreed definition for metaheuristics. The definition used in the Metaheuristics Network is the following.

A metaheuristic is a set of concepts that can be used to define heuristic methods that can be applied to a wide set of different problems. In other words, a metaheuristic can be seen as a general algorithmic framework which can be applied to different optimization problems with relatively few modifications to make them adapted to a specific problem. Examples of metaheuristics include simulated annealing (SA), tabu search (TS), iterated local search (ILS), evolutionary algorithms (EC), and ant colony optimization (ACO). Project Summary, Metaheuristics Network, Improving Human Potential, European Community   http://www.metaheuristics.org/index.php?main=1 

molecular information theory: In our laboratory we use Claude Shannon's information theory, computers (Unix, Pascal and PostScript graphics on Sun workstations) and genetic engineering (protein and DNA gels, cloning, sequencing and magnetic bead technology) to study genetic control patterns on DNA and RNA.  "Molecular Information Theory" Tom Schneider, National Cancer Institute, US, 2002   http://www.lecb.ncifcrf.gov/~toms/introduction.html

Molecular Information Theory and the theory of molecular machines, Tom Schneider, NCI, US http://www.lecb.ncifcrf.gov/~toms/

molecular pattern recognition: Developing computational methodologies for the analysis and interpretation of large-scale expression datasets generated by DNA  microarray experiments. Analysis of genome-wide expression patterns and their correlations with phenotypes of interest may provide unique insights into the structure of genetic networks and into biological processes not yet  understood at the molecular level. Whitehead/ MIT [US] Genome Center's  Molecular Pattern Recognition web site. http://www.genome.wi.mit.edu/MPR/index.html  

Broader term: pattern recognition.  Related terms Expression glossary

mosaic plots: A graphical alternative for qualitative, or categorical, data … display cross- classified data by constructing rectangles of area  proportional to the counts … likely to become more familiar [to scientists] and their use is likely to grow. Are to categorical variables what scatterplots are to continuous variables, and their purpose is the same, to find interesting patterns of association between variables. RD Meyer & D Book “Visualization of data” Current Opinion in Biotechnology 11:89- 1196, 2000

multivariate statistics: A set of statistical tools to analyze data (e.g., chemical and biological) matrices using regression and/ or pattern recognition techniques. [IUPAC Computational]

neural networks: Technique for optimizing a desired property given a set of items which have been previously characterized with respect to that property (the 'training set'). Features of members of the training set which correlate with the desired property are 'remembered and used to generate a model for selecting new items with the desired property or to predict the fit of an unknown member. [IUPAC Combinatorial Chemistry] 

Communication between statisticians and neural net researchers is often hindered by the different terminology used in the two fields. There is a comparison of neural net and statistical jargon in ftp://ftp.sas.com/pub/neural/jargon  

IEEE Neural Networks, Institute of Electrical and Electronics Engineers http://www.ieee-nns.org/

Neural Network FAQ ftp://ftp.sas.com/pub/neural/FAQ.html

Narrower terms:  artificial neural networks, probabilistic neural networks. Often uses  fuzzy logic; Related terms: artificial intelligence; In silico & molecular modeling glossary self- organizing maps   

nonparametric: See under parametric versus nonparametric methods:

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. 

See also normalization Microarrays glossary Narrower terms: thresholding

ontology, ontologies: Information management & interpretation glossary

paraphrase problem: Information management & interpretation glossary

parsing: Using algorithms to analyze data into components. Semantic parsing involves trying to figure out what the components mean. Lexical parsing refers to the process of deconstructing the data into components.

Narrower term: In silico & molecular modeling glossary gene parsing

Partial Least Squares PLS: Projection to latent structures (PLS) is a robust multivariate generalized regression method using projections to summarize multitudes of potentially collinear variables (Wold et al., 1993).  [IUPAC Computational]

pattern recognition PR: The identification of patterns in large data sets using appropriate mathematical methodologies.  Examples are principal component  analysis (PCA), SIMCA, partial least squares (PLS) and artificial neural  networks (ANN) (Rouvray, 1990; Van de Waterbeemd, 1995ab) [IUPAC  Computational] 

Narrower terms:  artificial neural  networks, molecular pattern recognition, principal component  analysis (PCA), SIMCA, partial least squares (PLS)

Pearson correlation: Commonly used similarity function which looks explicitly at the shape of the expression profile, avoiding the need to transform the data beforehand. It’s easiest to understand what this function does by using a different spatial representation of the data. Take two expression profiles and draw a scatter plot of corresponding values. In other words, pair the first value of the first profile with the first value of the second, the second value of the first profile with the second value of the second, and so forth. The Pearson correlation measures how well a straight line can be fit to the data. A correlation of +1 means the fit is perfect to a line that slants up, 0 means the fit is random, and –1 means the fit is perfect to a line that slants down.

predictive data mining; Combines pattern matching, influence relationships, time set correlations, and dissimilarity analysis to offer simulations of future data sets...these systems are capable of incorporating entire data sets into their working, and not just samples, which make their accuracy significantly higher ... used often in clinical trial analysis and in structure- function correlations. "Data mining" Nature Biotechnology Vol. 18: 237-238 Supp. Oct. 2000

Broader term: data mining

predictor: See under classifier

Principal Components Analysis PCA: Computational approach to reducing the complexity of, for example, a set of descriptors, by identifying those features which provide the major contributions to observed properties, and thus reducing the dimensionality of the relevant property space. [IUPAC Combinatorial Chemistry]

A data reduction method using mathematical techniques to identify patterns in a data matrix. The main element of this approach consists of the construction of a small set of new orthogonal, i.e., non- correlated, variables derived from a linear combination of the original variables. [IUPAC Computational]

Often confused with common factor analysis. [Neural Network FAQ Part 1] ftp://ftp.sas.com/pub/neural/FAQ.html

probabilistic neural networks:  Statsoft

probability: Probability web http://www.mathcs.carleton.edu/probweb/probweb.html

profile chart: A line graph that is a direct translation of a Cluster- format data table. Each cell of the data table is represented as a point whose Y coordinate indicates the expression value, and whose X coordinate is the ordinal position of the value in its profile. The points for each profile are connected by lines. A profile chart is a good way to visualize individual clusters.

Related terms: cluster analysis, dendogram, heat map

protein and mRNA data: Proteomics glossary

Quantitative Structure-Activity Relationships QSAR: In silico & molecular modeling glossary

recursive partitioning: Process for identifying complex structure- activity relationships in large sets by dividing compounds into a hierarchy of smaller and more homogeneous sub- groups on the basis of the statistically most significant descriptors. 

Related terms: clustering,  principal components analysis. [IUPAC Combinatorial Chemistry]

regression analysis: The use of statistical  methods for modeling a set of dependent variables, Y, in terms of combinations of  predictors, X. It includes methods such as multiple linear regression (MLR) and partial least squares (PLS). [IUPAC Computational]

resampling: See under bootstrapping 

regression to the mean: A common misconception about genetics has to do with overgeneralization about the likelihood of increased quality by selective breeding.  Two very tall parents will tend to produce offspring who are taller than the average population -- but less tall than the average of the parents' heights.  Or as George Bernard Shaw is supposed to have said to a famous beauty who suggested they have a child ""With your brains and my looks ..." He said to have replied, "But what if the child had my looks and your brains?" 

remembrance agents: Information management & interpretation glossary

robust:  A statistical test that yields approximately correct results despite the falsity of certain of the assumptions on which it is based  Oxford English Dictionary

Hence, can refer to a process which is relatively insensitive to human foibles and variables in the way (for example, an assay) is carried out. Idiot- proof.

SIMCA (SIMple Classification Analysis or Soft Independent Modeling of Class Analogy): This method is a pattern recognition and classification  technique (Dunn and Wold, 1995). [IUPAC Computational]

SPC Structure-Property Correlations: In silico & molecular modeling glossary

scalable, scaling: Drug discovery & development glossary

schema (plural schemata): A description of the data represented within a database. The format of the description varies but includes a table layout for a relational database or an entity- relationship diagram. Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary 

Narrower term: global schema

self-organization:  Typically refers to a process by which systems organize themselves without external direction, manipulation or control. The term is difficult to define precisely because it is used in reference to a variety of processes generating a variety of systems. M. Beth L. Dempster, Glossary, A Self- Organizing Systems Perspective on Organizing for Sustainability, Univ. of Waterloo, Canada, 1998 http://www.nesh.ca/jameskay/ersserver.uwaterloo.ca/jjkay/grad/bdempster/gloss.html  

A process where the organization (constraint, redundancy) of a system spontaneously increases, i.e. without this increase being controlled by the environment or an encompassing or otherwise external system. [F. Heylighen, "Self Organization" Jan 27, 1997  in: F. Heylighen, C. Joslyn and V. Turchin (editors): Principia Cybernetica Web (Principia Cybernetica, Brussels) http://pespmc1.vub.ac.be/SELFORG.html

self- organizing map: Similar to k-means, but the algorithm organizes the clusters in a two- dimensional grid, such that clusters that are close together in the grid are more similar than those further apart. This is a very useful feature when working with large numbers of clusters.

A type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization … Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation. [P. Tamayo et al “Interpreting patterns of gene expression with self- organizing maps: methods and application to hematopoietic differentiation” PNAS 96(6): 2907- 2912 Mar 16, 1999] 

Related term: neural networks  

semantic data integration: Information management & interpretation glossary

Google = about  214 July 19, 2002; about 24, 200 Oct 8, 2007

semantic parsing: See under parsing

Google = about 1,380 Aug. 20, 2002; about 45,200 Oct 8, 2007

sequencing algorithms: See Sequencing Glossary BLAST, FASTA, Needleman - Wunsch, Smith - Waterman   

similarity scores: See under distance functions or similarity scores: 

simulated annealing: In silico & molecular modeling

stochastic: "Aiming, proceeding by guesswork" (Webster's Collegiate Dictionary). Term which is often applied to combinatorial processes involving true random sampling, such as selection of beads from an encoded library, or certain methods for library design. [IUPAC COMBINATORIAL CHEMISTRY]

Truly random, based on probability.

Structure Activity Relationship SAR: Drug discovery & development; Narrower terms 3D-QSAR, QSAR

Support Vector Machines SVMs: A new generation learning system based on recent advances in statistical learning theory. SVMs deliver state- of- the- art performance in real- world applications such as text categorisation, hand- written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel- based Learning Methods, Cambridge University Press, 2000  http://uk.cambridge.org/engineering/catalogue/0521780195/default.htm

taxonomy, taxonomies: Information management & interpretation glossary

text mining: Information management & interpretation glossary

thresholding: The researcher defines minimum and maximum values that are considered reliable; measurements that are too low or too high are dropped from the dataset or marked as unreliable. It also makes sense to subtract the minimum value from all other measurements, because this reflects baseline noise. This approach implicitly assumes that microarrays normally operate in the linear part of the dynamic range, and that the transitions between the linear and flat regimes occur abruptly. 

Broader term: normalization

time delay data mining: The data is collected over time and systems are designed to look for patterns that are confirmed or rejected as the data set increases and becomes more robust.  This approach is geared toward long- term clinical trial analysis and multicomponent mode of action studies. "Data mining" Nature Biotechnology Vol. 18: 237-238 Supp. Oct. 2000 

Broader term: data mining 

training set: An initial dataset for which the correct answers are known and feeding the data and correct answers into a program that adjusts the parameters of the general model. The training program adjusts the model parameters so that the model works well on the given dataset. There are usually enough parameters so that this can be accomplished, provided the dataset is reasonably consistent. The training set usually has to be very large to produce a good classifier.  

trends-based data mining: Software analyzes large and complex data sets in terms of any changes that occur in specific data sets over time.  Data sets can be user- defined or the system can uncover them itself...This is especially important in cause- and- effect biological experiments.  Screening is a good example.  Data mining, Nature Biotechnology Vol. 18: 237- 238 Supp. Oct. 2000

Broader term: data mining

unsupervised training sets: Unsupervised training is where the network has to make sense of the inputs without outside help. ... Unsupervised training is used to perform some initial characterization on inputs. However, in the full blown sense of being truly self learning, it is still just a shining promise that is not fully understood, does not completely work, and thus is relegated to the lab. Artificial Neural Networks Technology, Data and Analysis Software, Dept. of Defense, 2000 http://www.dacs.dtic.mil/techs/neural/neural3.html

visualization: Information management & interpretation glossary: 

well-posed problem: A problem is well-posed if (and only if): it has one and only one solution; a small change in the data (such as prescribed boundary conditions, source strengths, coefficients in the PDE, etc) produces only a small change in the solution. [Nils Andersson "Appropriate boundary conditions", Partial Differential Equations, Univ. of Southampton, UK, 2001] http://www.maths.soton.ac.uk/staff/Andersson/MA361/node38.html

Related term: ill-posed problems

IUPAC definitions are reprinted with the permission of the International Union of Pure and Applied Chemistry.

Bibliography
M. Beth L. Dempster, Glossary, A Self-Organizing Systems Perspective on Organizing for Sustainability, Univ. of Waterloo, Canada, 1998, 30 + terms. http://www.nesh.ca/jameskay/ersserver.uwaterloo.ca/jjkay/grad/bdempster/gloss.html
Evolutionary Algorithms, terms and definitions, Hans-Georg Beyer, Eva Brucherseifer, Wilfried Jakob, Hartmut Pohlheim, Bernhard Sendhoff, Thanh Binh To, 2002 http://ls11-www.cs.uni-dortmund.de/people/beyer/EA-glossary/ 
Flake Gary Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation. Glossary MIT Press, 2000. 280+ definitions. http://mitpress.mit.edu/books/FLAOH/cbnhtml/glossary-intro.html
Glossary of terms, Ron Kohavi, Machine Learning, 30, 271- 274, 1998, 45 definitions.  http://ai.stanford.edu/~ronnyk/glossary.html 
Inmon, Bill, Glossary of Data Warehousing, 2002-2005  http://www.inmoncif.com/library/glossary/  
IUPAC Combinatorial International Union of Pure and Applied Chemistry, Glossary of Terms Used in Combinatorial Chemistry, D. Maclean, J.J. Baldwin, V.T. Ivanov, Y. Kato, A. Shaw, P. Schneider, and E.M. Gordon, Pure Appl. Chem., Vol. 71, No. 12, pp. 2349- 2365, 1999, 100+ definitions  http://www.iupac.org/reports/1999/7112maclean/
IUPAC Computational] International Union of Pure and Applied Chemistry, Glossary of Terms used in Computational Drug Design, H. van de Waterbeemd, R.E. Carter, G. Grassy, H. Kubinyi, Y. C.. Martin, M.S. Tute, P. Willett, 1997. 125+ definitions. http://www.iupac.org/reports/1997/6905vandewaterbeemd/glossary.html
NIST National Institute of Standards and Technology, Dictionary of Algorithms, Data Structures and Problems, Paul Black, 2001, 1300+  terms  http://www.nist.gov/dads/
Statsoft, Inc. Statistics glossary, Electronic Statistics Textbook, Tulsa OK, US 2001, 1200 + definitions. http://www.statsoft.com/textbook/stathome.html
Tollenaere JP, EE Moret, Hyperglossary of [Molecular Modelling in Drug Design] Terminology, Utrecht University, 1996. 150+ definitions. http://wwwcmc.pharm.uu.nl/webcmc/glossary.html
Hao Zhang, A Statistical Learning/ Pattern Recognition Glossary, Univ. of Wisconsin - Madison, 1999, 80 terms.  http://www.cs.wisc.edu/~hzhang/glossary.html

Alpha glossary index

How to look for other unfamiliar  terms