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・ Graphic, Arkansas
・ Graphic.ly
・ Graphicacy
・ Graphical Data Display Manager
・ Graphical Editing Framework
・ Graphical Environment Manager
・ Graphical Evaluation and Review Technique
・ Graphical game theory
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・ Graphical Kernel System
・ Graphical language
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・ Graphical list of chief ministerial tenures and important events of Tamil Nadu
・ Graphical model
・ Graphical Modeling Framework
Graphical models for protein structure
・ Graphical Network Simulator-3
・ Graphical path method
・ Graphical projection
・ Graphical representations of two-way-contest opinion polling data from the United States presidential election, 2008
・ Graphical sound
・ Graphical system design
・ Graphical timeline from Big Bang to Heat Death
・ Graphical timeline of prehistoric life
・ Graphical timeline of the Big Bang
・ Graphical timeline of the Stelliferous Era
・ Graphical timeline of the universe
・ Graphical tools
・ Graphical unitary group approach
・ Graphical user interface


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Graphical models for protein structure : ウィキペディア英語版
Graphical models for protein structure

Graphical models have become powerful frameworks for protein structure prediction, protein–protein interaction and free energy calculations for protein structures. Using a graphical model to represent the protein structure allows the solution of many problems including secondary structure prediction, protein protein interactions, protein-drug interaction, and free energy calculations.
There are two main approaches to use graphical models in protein structure modeling. The first approach uses discrete variables for representing coordinates or dihedral angles of the protein structure. The variables are originally all continuous values and, to transform them into discrete values, a discretization process is typically applied. The second approach uses continuous variables for the coordinates or dihedral angles.
==Discrete graphical models for protein structure==
Markov random fields, also known as undirected graphical models are common representations for this problem. Given an undirected graph ''G'' = (''V'', ''E''), a set of random variables ''X'' = (''X''''v'')''v'' ∈ ''V'' indexed by ''V'', form a Markov random field with respect to ''G'' if they satisfy the pairwise Markov property:
*any two non-adjacent variables are conditionally independent given all other variables:
:X_u \perp\!\!\!\perp X_v | X_ \ \notin E.
In the discrete model, the continuous variables are discretized into a set of favorable discrete values. If the variables of choice are dihedral angles, the discretization is typically done by mapping each value to the corresponding rotamer conformation.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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