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  Clinical informatics   Drug discovery informatics   IT infrastructure   Ontologies   Research
Technologies: Microarrays & protein chips   Sequencing
Biology: Protein Structures   Sequences, DNA & beyond.  

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 

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

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

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  http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-13.html  Related term: high-dimensionality

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

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. 

evolutionary algorithm: An umbrella term used to describe computer-based problem solving systems which use computational models of some of the known mechanisms of EVOLUTION as key elements in their design and implementation. A variety of EVOLUTIONARY Algorithms have been proposed. The major ones are: GENETIC Algorithms (see Q1.1), EVOLUTIONARY PROGRAMMING (see Q1.2), EVOLUTION Strategies (see Q1.3), CLASSIFIER Systems (see Q1.4), and GENETIC PROGRAMMING (see Q1.5). They all share a common conceptual base of simulating the evolution of INDIVIDUAL structures via processes of SELECTION, MUTATION, and REPRODUCTION. The processes depend on the perceived PERFORMANCE of the individual structures as defined by an ENVIRONMENT.

More precisely, EAs maintain a POPULATION of structures, that evolve according to rules of selection and other operators, that are referred to as "search operators", (or GENETIC Operators), such as RECOMBINATION and mutation. Each individual in the population receives a measure of its FITNESS in the environment. Reproduction focuses attention on high fitness individuals, thus exploiting (cf. EXPLOITATION) the available fitness information. Recombination and mutation perturb those individuals, providing general heuristics for EXPLORATION. Although simplistic from a biologist's viewpoint, these algorithms are sufficiently complex to provide robust and powerful adaptive search mechanisms. Heitkötter, Jörg and Beasley, David, eds. (2001) "The Hitch-Hiker's Guide to Evolutionary Computation: A list of Frequently Asked Questions (FAQ)", USENET: comp.ai.genetic  Available via anonymous FTP from ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/genetic/

evolutionary computation: Encompasses methods of simulating EVOLUTION 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 GENETIC Algorithms, EVOLUTION 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.  Heitkötter, Jörg and Beasley, David, eds. (2001) "The Hitch-Hiker's Guide to Evolutionary Computation: A list of Frequently Asked Questions (FAQ)", USENET: comp.ai.genetic  Available via anonymous FTP from ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/genetic/ 

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.

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

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

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]

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

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/

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

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

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

metadata: Information management & interpretation

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 

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  Narrower terms:  artificial neural networks, probabilistic neural networks. Often uses  fuzzy logic; Related terms: artificial intelligence; Drug discovery informatics self- organizing maps   
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

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 Narrower terms: thresholding

OASIS Organization for the Advancement of Structured Information Systems: A not- for- profit, global consortium that drives the development, convergence and adoption of e-business standards. http://www.oasis-open.org/who/  
OASIS Glossary of terms http://www.oasis-open.org/glossary/index.php  50 + terms

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: Drug discovery informatics  gene parsing

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

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

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?" 

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.

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

semantic parsing: See under parsing Google = about 1,380 Aug. 20, 2002; about 45,200 Oct 8, 2007

similarity scores: See under distance functions or similarity scores: 

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.

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

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/  
Heitkötter, Jörg and Beasley, David, eds. (2001) "The Hitch-Hiker's Guide to Evolutionary Computation: A list of Frequently Asked Questions (FAQ)", USENET: comp.ai.genetic  Available via anonymous FTP from ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/genetic/ About 110 pages  
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

Alpha glossary index

How to look for other unfamiliar  terms