Fuzzy Logic Github

[Quincey Homer]

Thefollowing code provides the use of a conventional method in a Restaurant Tipping Problem. There are many sections to go through and understand the process with steady progress in the coding. However, it can be better solved using fuzzy logic. It is uploaded to understand the implementation of such a problem if fuzzy logic is not used and very basic of Matlab needs to be used.

CellNOpt (from CellNetOptimizer; a.k.a. CNO) is a software used for creating logic-based models of signal transduction networks using different logic formalisms (Boolean, Fuzzy, or differential equations). CellNOpt uses information on signaling pathways encoded as a Prior Knowledge Network, and trains it against high-throughput biochemical data to create cell-specific models.

CellNOpt is freely available under GPL license in R and Matlab languages. It can be also accessed through a python wrapper, and a Cytoscape plugin called CytoCopter provides a graphical user interface.

A series of packages are available in R. The core CellNOpt is available on BioConductor web site: CellNOptR, revision 1.40.0. The most recent updates from Gjerga, Trairatphisan, Gabor et al. 2020 are available here.

CellNOptR contains the core functions as well as the boolean and steady states version. It implements the workflow described in Saez-Rodriguez et al Mol Sys Bio 2009, with extended capabilities for multiple time points.

CNORprob is a probabilistic logic variant of CellNOpt which allows for quantitative optimisation of logical network for (quasi-)steady-state data as described in Gjerga, Trairatphisan, Gabor et al 2020.

CNORfeeder is an add-on to CellNOptR that permits to extend a network derived from literature with links derived in a strictly data-driven way and supported by protein-protein interactions as described in (Eduati et al Bioinformatics 2012). The most recent version of CNORfeeder, which can also be applied to timecourse data with a logic ordinary differential equations (ODE) formalism can be found here (Gjerga, Trairatphisan, Gabor et al 2020).

CellNOptR-MaBoSS is a method for training boolean logic models of signalling networks using prior knowledge networks and perturbation data with a stochastic simulator (Gjerga, Trairatphisan, Gabor et al 2020).

A Python package called cellnopt.wrapper provides a python interace to the R packages (CellNOptR, CNORode and CNORfuzzy). It uses rpy2 and is available on Pypi. For more details see the sphinx documentation in the ./doc directory after downloading the wrapper. In addition a pure Python version is developed on github.

MEIGO, a global optimization toolbox that includes a number of metaheuristic methods as well as a Bayesian inference method for parameter estimation, that can be applied to model training in CellNOpt. Available in R, Matlab, and Python. Presented in Egea et al BMC Bioinformatics, 214.

The ColoMoTo consortium involves other groups developing tools and methods for logic modelling. We have also jointly develop the standard SBML-qual (Chaouiya et al, BMC Syst Bio 2013) that allows to exchange models within tools.

Some extra materials and courses about the formats used can be found in the CNODocs. Besides, the following link provides a tutorial given at In Silico Systems Biology, 2013. The following link provides also a CytoCopteR tutorial.

C Terfve, T Cokelaer, A MacNamara, D Henriques, E Goncalves, MK Morris, M van Iersel, DA Lauffenburger, J Saez-Rodriguez. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology, 2012, 6:133 PDF

In Boolean or two-valued logic, the truth values of a variable will always be either 0 or 1 and in traditional two-valued set theory, an element belongs to a set or not. Similar to this, in a typical classification problem, an observation is classified into one of several different classes. In all of these cases, there is a definitive true value.

Fuzzy logic presents a different approach to these problems. In fuzzy logic, the truth value of a variable or the label (in a classification problem) is a real number between 0 and 1. For example, suppose you are in a pool with a friend. For you, the water is warm and for your friend, the water is cold. After a silly discussion between both of you, you decide to use a thermometer to measure the temperature, and it is 20C. So since both of you know the freezing and boiling point of water (0C and 100C), you said the water is 0.20 hot and your friend is said, it is 0.80 cold. In other words, for you and your friend, there is not an absolute truth about the state of the water.

Fuzzy Logic H is arranged in two independent columnswith identical functionality.The left column (described below)applies the fuzzy logic operators to the A and B inputs.The right column applies the same operators to the C and D inputs.

Disconnected inputs.Leave one input disconnected.Fuzzy Logic H treates a disconnected inputas a 0V input signal.How it interprets 0V depends on the position of the range switch.In UNI range (0V to 10V),0V means absolutely false.In BI range (-5V to 5V),0V means half true.

Feedback Loops.Connect one or both inputs of each columnto outputs from the other column.This can generate very complex output signalsfrom simple input signals.WARNING:If any input voltage is above or below the selected range,feedback loops like this can generatevery high output voltages.

It may be interesting to considerthe meaningof signals above or below the selected input range.More true than absolutely true?More false than absolutely false?If you connect input signals outside the selected range,be sure to heed the warnings below.

Gender violence is one of the most serious and widespread problems in our society. In dangerous cases, the use of special devices for GPS tracing is recommended in some countries. However, these devices are used only in extreme cases and have many drawbacks. This work describes a new system to combat gender violence that tries to improve the existing system. It combines different location schemes based on distinct technologies to determine the distance between victim and aggressor. Besides, the application is enriched with a fuzzy-logic-based approach as a way to avoid false alarms. If an offender gets close to a victim, even if the established set distance has not been broken yet, the victim is warned thanks to the application. Moreover, if the fuzzy logic based approach confirms that the pre-set distance has been broken, an automatic streaming of a real-time video starts to be sent to the police, and some stored contacts are warned so that they can try to protect the victim until the police arrive. A beta-version has been implemented and the obtained results are promising.

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