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2014-02-11
#Logistic_regression
https://en.wikipedia.org/wiki/Logistic_regression
#Logistic_regression
logit regression
#Logistic_regression
type of probabilistic classification model used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features).
#Logistic_regression
logistic regression
#MAMO_0000003
http://en.wikipedia.org/wiki/Mathematical_model
#MAMO_0000003
Description of a system using mathematical concepts and language. The model is composed of a set of variables and a set of equations that establish relationships between the variables.
#MAMO_0000003
A set of ordinary differential equation describing a physical process.
#MAMO_0000003
mathematical model
#MAMO_0000004
http://en.wikipedia.org/wiki/Statistical_model
#MAMO_0000004
Model that describes how one or more random variables are related to one or more random variables.
#MAMO_0000004
The relationship between the size and the age.
#MAMO_0000004
statistical model
#MAMO_0000008
http://en.wikipedia.org/wiki/Steady_state_%28chemistry%29
#MAMO_0000008
Model which describes a system where the state variable values do not vary.
#MAMO_0000008
steady-state model
#MAMO_0000009
Can be analysed using flux balance analysis [http://identifiers.org/biomodels.kisao/KISAO_0000437].
Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol, 185(9):2692-9.
#MAMO_0000009
http://identifiers.org/pubmed/12700248
#MAMO_0000009
The constraint-based modeling procedure does not strive to find a single solution but rather finds a collection of all allowable solutions to the governing equations that can be defined (a solution space). The subsequent application of additional constraints further reduces the solution space and, consequently, reduces the number of allowable solutions that a cell can utilize.
#MAMO_0000009
constraint-based model
#MAMO_0000010
http://en.wikipedia.org/wiki/Variable_%28mathematics%29
#MAMO_0000010
Value that may change within the scope of a given model or set of operations.
#MAMO_0000010
variable
#MAMO_0000017
Synonyms: input variable
#MAMO_0000017
http://en.wikipedia.org/wiki/Dependent_and_independent_variables;
#MAMO_0000017
http://www.ncsu.edu/labwrite/po/independentvar.htm
#MAMO_0000017
variable which is controlled in an experiment, and that affects other variables during the experiment. An independent variable does not depend on other variables of the model.
#MAMO_0000017
independent variable
#MAMO_0000018
Synonyms: output variable
#MAMO_0000018
http://en.wikipedia.org/wiki/Dependent_and_independent_variables;
#MAMO_0000018
http://www.ncsu.edu/labwrite/po/dependentvar.htm
#MAMO_0000018
variable which is measured in an experiment, and that is affected during the experiment. A dependent variable depends on other variables
#MAMO_0000018
Example: in an experiment one controls x and measure y, with a relationship y = ax+b, y is depending on x.
#MAMO_0000018
dependent variable
#MAMO_0000019
http://en.wikipedia.org/wiki/Pharmacokinetics
#MAMO_0000019
Model dedicated to the determination of the fate of substances administered externally to a living organism. Pharmacokinetics models are divided into several areas including the extent and rate of absorption, distribution, metabolism and excretion (ADME) to which Liberation is sometimes added (LADME).
#MAMO_0000019
pharmacokinetics model
#MAMO_0000020
http://en.wikipedia.org/wiki/Pharmacodynamics
#MAMO_0000020
Models dedicated to the study of the biochemical and physiological effects of drugs on the body or on microorganisms or parasites within or on the body and the mechanisms of drug action and the relationship between drug concentration and effect.
#MAMO_0000020
pharmacodynamics model
#MAMO_0000021
http://en.wikipedia.org/wiki/Multiphysics
#MAMO_0000021
Multiphysics treats simulations that involve multiple physical models or multiple simultaneous physical phenomena. For example, combining chemical kinetics and fluid mechanics or combining finite elements with molecular dynamics. Multiphysics typically involves solving coupled systems of partial differential equations.
#MAMO_0000021
multiphysics model
#MAMO_0000022
Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns.
#MAMO_0000022
http://en.wikipedia.org/wiki/Rule-based_modeling#For_biochemical_systems
#MAMO_0000022
Model that uses a set of rules used to describe other model instances. The rule-set can be used to create a model, or suitable tools can use a rule-set in place of a model.
#MAMO_0000022
rule-based model
#MAMO_0000023
http://en.wikipedia.org/wiki/Computational_model
#MAMO_0000023
mathematical model that requires computer simulations to study the behavior of a complex system. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments.
#MAMO_0000023
weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, neural network models.
#MAMO_0000023
computational model
#MAMO_0000024
Synonym: multi-agent model
#MAMO_0000024
http://en.wikipedia.org/wiki/Agent-based_model
#MAMO_0000024
model simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole
#MAMO_0000024
agent-based model
#MAMO_0000025
http://en.wikipedia.org/wiki/Petri_net
#MAMO_0000025
directed bipartite graph, in which the nodes represent transitions (i.e. events that may occur, signified by bars) and places (i.e. conditions, signified by circles). The directed arcs describe which places are pre- and/or postconditions for which transitions (signified by arrows) occurs.
#MAMO_0000025
Petri net
#MAMO_0000026
http://en.wikipedia.org/wiki/Computational_neuroscience
#MAMO_0000026
Computational Neuroscience emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
#MAMO_0000026
computational Neuroscience model
#MAMO_0000027
information obtained about a system by the application of an analysis procedure to a model of this system
#MAMO_0000027
timecourse of a concentation; EC50
#MAMO_0000027
readout
#MAMO_0000028
http://en.wikipedia.org/wiki/Population_models
#MAMO_0000028
type of mathematical model that is applied to the study of population dynamics.
#MAMO_0000028
population model
#MAMO_0000029
http://en.wikipedia.org/wiki/Matrix_population_models
#MAMO_0000029
specific type of population model that uses matrix algebra. Matrix algebra, in turn, is simply a form of algebraic shorthand for summarizing a larger number of often repetitious and tedious algebraic computations.
#MAMO_0000029
matrix population model
#MAMO_0000030
model where the discrete values of variables is determined by logical combinations of the values of other variables.
#MAMO_0000030
logical model
#MAMO_0000031
evolution of a variable value over time
#MAMO_0000031
timecourse
#MAMO_0000032
model which take into account the spatial distribution or geometric characteristics of the entities described by its variables.
#MAMO_0000032
spatial model
#MAMO_0000033
http://en.wikipedia.org/wiki/Reaction_diffusion_model
#MAMO_0000033
mathematical model which explains how the concentration of one or more substances distributed in space changes under the influence of two processes: local chemical reactions in which the substances are transformed into each other, and diffusion which causes the substances to spread out over a domain in space.
#MAMO_0000033
reaction diffusion model
#MAMO_0000034
http://en.wikipedia.org/wiki/Finite_Element_Method
#MAMO_0000034
model where the space is split into a number of subspaces (sometimes called voxels) that are each considered homegenous and isotropic.
#MAMO_0000034
finite element spatial model
#MAMO_0000035
network model
#MAMO_0000036
http://en.wikipedia.org/wiki/Biochemical_system
#MAMO_0000036
Biochemical network studies chemical processes within and relating to, living organisms.
#MAMO_0000036
biochemical network
#MAMO_0000037
Purposeful simplification of reality, designed to imitate certain phenomena or characteristics of a system while downplaying non-essential aspects. Its value lies in the ability to generalise insights from the model to a broader class of systems.
#MAMO_0000037
model type
Albert R, Wang R (2009) Discrete dynamic modeling of cellular signaling networks. Methods Enzymol 467:281–306
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http://identifiers.org/pubmed/19897097
#MAMO_0000038
Signal transduction is a process for cellular communication where the cell receives (and responds to) external stimuli from other cells and from the environment.
#MAMO_0000038
signalling network
Schlitt T, Brazma A (2007) Current approaches to gene regulatory network modelling. BMC Bioinf 8(Suppl 6):S9
#MAMO_0000039
http://identifiers.org/pubmed/17903290
#MAMO_0000039
Gene regulation controls the expression of genes and, consequently, all cellular functions. Gene expression is a process that involves transcription of the gene into mRNA, followed by translation to a protein, which may be subject to post-translational modification. The transcription process is controlled by transcription factors (TFs) that can work as activators or inhibitors. TFs are themselves encoded by genes and subject to regulation, which altogether forms complex regulatory networks.
#MAMO_0000039
gene regulatory network
Palsson B (2006) Systems Biology: Properties of Reconstructed Networks.
Cambridge University Press
#MAMO_0000040
http://identifiers.org/isbn/9780521859035
#MAMO_0000040
Metabolism is a mechanism composed by a set of biochemical reactions, by which the cell sustains its growth and energy requirements. It includes several catabolic and anabolic pathways of enzyme-catalyzed reactions that import substrates from the environment and transform them into energy and building blocks required to build the cellular components. Metabolic pathways are interconnected through intermediate metabolites, forming complex networks.
#MAMO_0000040
metabolic network
#MAMO_0000041
http://en.wikipedia.org/wiki/Bayesian_network
#MAMO_0000041
Bayes model
#MAMO_0000041
Bayes network
#MAMO_0000041
Bayesian network
#MAMO_0000041
belief network
#MAMO_0000041
probabilistic DAG model
#MAMO_0000041
probabilistic directed acyclic graphical model
#MAMO_0000041
Bayesian networks are a special type of probabilistic graphs. Their nodes represent random variables (discrete or continuous) and the edges represent conditional dependencies, forming a directed acyclic graph. Each node contains a probabilistic function that is dependent on the values of its input nodes.
#MAMO_0000041
Bayesian model
Zou M, Conzen S (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21(1):71–79
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http://identifiers.org/pubmed/15308537
#MAMO_0000043
dynamic Bayes model
#MAMO_0000043
dynamic Bayes network
#MAMO_0000043
dynamic Bayesian network
#MAMO_0000043
dynamic belief network
#MAMO_0000043
dynamic probabilistic DAG model
#MAMO_0000043
dynamic probabilistic directed acyclic graphical model
#MAMO_0000043
A dynamic Bayesian network is a Bayesian network that overcomes the inability to model feedback loops. In this case, the variables are replicated for each time step and the feedback is modeled by connecting the nodes at adjacent time steps.
#MAMO_0000043
dynamic Bayesian model
#MAMO_0000044
http://en.wikipedia.org/wiki/Process_calculus
#MAMO_0000044
process calculus
#MAMO_0000044
Process algebras are a family of formal languages for modeling concurrent systems. They generally consist on a set of process primitives, operators for sequential and parallel composition of processes, and communication channels.
#MAMO_0000044
process algebra
#MAMO_0000045
http://en.wikipedia.org/wiki/Differential_equation
#MAMO_0000045
Differential equations describe the rate of change of continuous variables. They are typically used for modeling dynamical systems in several areas.
#MAMO_0000045
differential equation model
#MAMO_0000046
http://en.wikipedia.org/wiki/Ordinary_differential_equation
#MAMO_0000046
ODEs have been used in systems biology to describe the variation of the amount of species in the modeled system as a function of time.
#MAMO_0000046
ODEs model
#MAMO_0000046
ordinary differential equations model
#MAMO_0000047
http://en.wikipedia.org/wiki/Stochastic_differential_equation
#MAMO_0000047
SDEs model
#MAMO_0000047
SDEs can be used to account for stochastic effects.
#MAMO_0000047
stochastic differential equations model
#MAMO_0000048
http://en.wikipedia.org/wiki/Partial_differential_equation
#MAMO_0000048
PDEs model
#MAMO_0000048
PDEs can be used to account for spatial distribution.
#MAMO_0000048
partial differential equations model
#MAMO_0000049
http://en.wikipedia.org/wiki/Finite-state_machine
#MAMO_0000049
Interacting state machines are diagram-based formalisms that describe the temporal behavior of a system based on the changes in the states of its parts. They differ from other approaches as they define a system in terms of its states rather than its components.
#MAMO_0000049
interacting state machine
#MAMO_0000050
http://en.wikipedia.org/wiki/Cellular_automaton
#MAMO_0000050
Cellular automata are discrete dynamic models that consist on a grid of cells with a finite number of states. A cellular automaton has an initial configuration that changes at each time step through a predefined rule that calculates the state of each cell as a function of the state of its neighbors at the previous step.
#MAMO_0000050
cellular automaton
Wishart DS, Yang R, Arndt D, Tang P, Cruz J (2005) Dynamic cellular automata: an alternative approach to cellular simulation. In Silico Biol., 5(2):139-61.
#MAMO_0000051
identifiers.org/pubmed/15972011
#MAMO_0000051
DSA
#MAMO_0000051
Dynamic cellular automata are a variation of cellular automata that allows for movement of the cell contents inside the grid, mimicking brownian motion.
#MAMO_0000051
dynamic cellular automaton
#MAMO_0000052
http://en.wikipedia.org/wiki/Stochastic_cellular_automaton
#MAMO_0000052
Cellular automaton whose updating rule is stochastic, which means the new entity's state is not chosen deterministically based on the neighbours' states, but according to some probability distributions depending on the neighbours' states.
#MAMO_0000052
stochastic cellular automaton
#MAMO_0000053
http://en.wikipedia.org/wiki/Boolean_network
Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467
#MAMO_0000053
http://identifiers.org/pubmed/5803332
#MAMO_0000053
Boolean models model networks of genes by boolean variables that represent active and inactive states. At each time step, the state of each gene is determined by a logic rule which is a function of the state of its regulators. The state of all genes forms a global state that changes synchronously.
#MAMO_0000053
boolean model
#MAMO_0000054
modeling entity feature
#MAMO_0000054
Dependent entity which another modelling entity has, i.e., that entity exhibits its property. Other common terms for property in natural language are characteristic, property, quality, etc.
#MAMO_0000054
modelling entity feature
#MAMO_0000055
http://en.wikipedia.org/wiki/Temporality
#MAMO_0000055
Characterises the evolution of a modelling entity over time.
#MAMO_0000055
temporal quality
#MAMO_0000056
http://en.wikipedia.org/wiki/Dynamical
#MAMO_0000056
Characterises a modelling entity that evolves over time.
#MAMO_0000056
dynamical characteristic
#MAMO_0000057
http://en.wiktionary.org/wiki/static
#MAMO_0000057
Characterises a modelling entity that does not evolve over time.
#MAMO_0000057
static characteristic
#MAMO_0000058
http://en.wiktionary.org/wiki/qualitative
#MAMO_0000058
http://en.wiktionary.org/wiki/quantitative
#MAMO_0000058
Characterises the possibility to be measured numerically.
#MAMO_0000058
quantitative characteristic
#MAMO_0000059
http://en.wiktionary.org/wiki/discrete
#MAMO_0000059
Which values can be enumerated.
#MAMO_0000059
discrete characteristic
#MAMO_0000060
http://en.wiktionary.org/wiki/continuous
#MAMO_0000060
Which values cannot be enumerated. Whatever two values, there is always another value in between.
#MAMO_0000060
continuous characteristic
#MAMO_0000061
http://en.wikipedia.org/wiki/Nonlinear
#MAMO_0000061
http://en.wikipedia.org/wiki/Linear_system
#MAMO_0000061
Which satisfies the principles of superposition and scaling.
#MAMO_0000061
linear quality
#MAMO_0000062
http://en.wikipedia.org/wiki/Uncertainty
#MAMO_0000062
Characterises the certainty, or lack of, of the modelling entity feature.
#MAMO_0000062
uncertainty level
#MAMO_0000063
http://en.wikipedia.org/wiki/Deterministic
#MAMO_0000063
Which value or behaviour is certain.
#MAMO_0000063
deterministic nature
#MAMO_0000064
http://en.wikipedia.org/wiki/Probabilistic
#MAMO_0000064
stochastic nature
#MAMO_0000064
Which can exhibit alternative values or behaviours with differnent probability.
#MAMO_0000064
probabilistic nature
#Multinomial_logistic_regression
https://en.wikipedia.org/wiki/Multinomial_logistic_regression
#Multinomial_logistic_regression
multinomial logit
#Multinomial_logistic_regression
softmax regression
#Multinomial_logistic_regression
regression model which generalizes logistic regression by allowing more than two discrete outcomes.
#Multinomial_logistic_regression
multinomial logistic regression
#Poisson_regression
https://en.wikipedia.org/wiki/Poisson_regression
#Poisson_regression
form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters.
#Poisson_regression
Poisson regression
#binary_data
https://en.wikipedia.org/wiki/Binary_data
#binary_data
Binary data is data whose unit can take on only two possible values, traditionally termed 0 and 1 in accordance with the binary numeral system and Boolean algebra.
#binary_data
binary data
#categorical_data
https://en.wikipedia.org/wiki/Categorical_data
#categorical_data
In statistics, categorical data is a statistical data type consisting of categorical variables, used for observed data whose value is one of a fixed number of nominal categories, or for data that has been converted into that form, for example as grouped data.
#categorical_data
categorical data
#coloured_Petri_net
http://en.wikipedia.org/wiki/Coloured_Petri_net
#coloured_Petri_net
http://identifiers.org/isbn/978-3-642-00284-7
#coloured_Petri_net
CP-net
#coloured_Petri_net
CPN
#coloured_Petri_net
colored Petri net
#coloured_Petri_net
graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role
#coloured_Petri_net
coloured Petri net
#continuous_Petri_net
http://identifiers.org/isbn/978-3-642-10669-9
#continuous_Petri_net
continuous PN
#continuous_Petri_net
Petri net model in which the number of marks in the places are real numbers instead of integers
#continuous_Petri_net
continuous Petri net
#count_data
https://en.wikipedia.org/wiki/Count_variable
#count_data
In statistics, count data is a statistical data type, a type of data in which the observations can take only the non-negative integer values {0, 1, 2, 3, ...}, and where these integers arise from counting rather than ranking.
#count_data
count data
#functional_Petri_net
http://identifiers.org/pubmed/17626066
#functional_Petri_net
functional PN
#functional_Petri_net
functional PNs
#functional_Petri_net
self-modified PN
#functional_Petri_net
self-modified PNs
#functional_Petri_net
self-modified Petri net
#functional_Petri_net
A Petri net that allows the flow relations between places and transitions to depend on the marking.
#functional_Petri_net
functional Petri net
#hasFeature
hasFeature
#hybrid_Petri_net
http://identifiers.org/pubmed/17626066
#hybrid_Petri_net
HPN
#hybrid_Petri_net
HPNs
#hybrid_Petri_net
hybrid PN
#hybrid_Petri_net
Petri nets that allow the coexistence of both continuous and discrete processes. They include discrete places (marked with tokens) and continuous places associated with real variables (e.g. concentration levels).
#hybrid_Petri_net
hybrid Petri net
#hybrid_functional_Petri_net
http://identifiers.org/pubmed/12954096
#hybrid_functional_Petri_net
HFPN
#hybrid_functional_Petri_net
HFPNs
#hybrid_functional_Petri_net
Hybrid functional PNs
#hybrid_functional_Petri_net
hybrid functional PN
#hybrid_functional_Petri_net
hybrid Petri nets with additional features: continuous transition firing rates can depend on the values of the input places and the weights of arcs can be defined as a function of the markings of the connected places.
#hybrid_functional_Petri_net
hybrid functional Petri net
#isUsedToModel
isUsedToModel
#lacksFeature
?X subclassOf: not (hasFeature some ?Y)
#lacksFeature
An object property to be used in the OBO version of MAMO to express nagation.
#lacksFeature
lacksFeature
#longitudinal_data
http://en.wikipedia.org/wiki/Longitudinal_study
#longitudinal_data
repeated observations of the same variables over time.
#longitudinal_data
longitudinal data
#mixed_model
http://en.wikipedia.org/wiki/Mixed_model
#mixed_model
statistical model containing both fixed effects and random effects, that is mixed effects.
#mixed_model
mixed model
#owlDef
owlDef
#statistical_variable
https://en.wikipedia.org/wiki/Statistical_data_type
#statistical_variable
In statistics, groups of individual data points may be classified as belonging to any of various statistical data types, e.g. categorical ("red", "blue", "green"), real number (1.68, -5, 1.7e+6), etc.
#statistical_variable
statistical data
#stochastic_Petri_net
http://en.wikipedia.org/wiki/Stochastic_Petri_net
#stochastic_Petri_net
SPN
#stochastic_Petri_net
stochastic PN
#stochastic_Petri_net
form of Petri net where the transitions fire after a probabilistic delay determined by a random variable.
#stochastic_Petri_net
stochastic Petri net
http://www.w3.org/2004/02/skos/core#altLabel
The range of skos:altLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#altLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties.
http://www.w3.org/2004/02/skos/core#altLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#altLabel
alternative label
http://www.w3.org/2004/02/skos/core#altLabel
An alternative lexical label for a resource.
http://www.w3.org/2004/02/skos/core#altLabel
Acronyms, abbreviations, spelling variants, and irregular plural/singular forms may be included among the alternative labels for a concept. Mis-spelled terms are normally included as hidden labels (see skos:hiddenLabel).
http://www.w3.org/2004/02/skos/core#definition
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#definition
definition
http://www.w3.org/2004/02/skos/core#definition
A statement or formal explanation of the meaning of a concept.
http://www.w3.org/2004/02/skos/core#example
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#example
example
http://www.w3.org/2004/02/skos/core#example
An example of the use of a concept.
http://www.w3.org/2004/02/skos/core#hiddenLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties.
http://www.w3.org/2004/02/skos/core#hiddenLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#hiddenLabel
hidden label
http://www.w3.org/2004/02/skos/core#hiddenLabel
A lexical label for a resource that should be hidden when generating visual displays of the resource, but should still be accessible to free text search operations.
http://www.w3.org/2004/02/skos/core#hiddenLabel
The range of skos:hiddenLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#prefLabel
A resource has no more than one value of skos:prefLabel per language tag, and no more than one value of skos:prefLabel without language tag.
http://www.w3.org/2004/02/skos/core#prefLabel
The range of skos:prefLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#prefLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise
disjoint properties.
http://www.w3.org/2004/02/skos/core#prefLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#prefLabel
preferred label
http://www.w3.org/2004/02/skos/core#prefLabel
The preferred lexical label for a resource, in a given language.