CCModel-class(procoil)
CCModel-class()所属R语言包：procoil
Class "CCModel"
类“CCModel”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
S4 class representing a coiled coil prediction model
中S4中类代表一个卷曲预测模型
类的对象----------Objects from the Class----------
In principle, objects of this class can be created by calls of the form new("CCModel"), although it is probably never necessary to create such an object from scratch. The default model is stored in the object PrOCoilModel. An alternative model, PrOCoilModelBA, that is optimized for balanced accuracy is available too (see below). For future safety, other models can be loaded from files using the function readCCModel.
的原则，在这个类的对象可以通过检测的形式new("CCModel")创建，虽然它可能是从来没有必要从头开始创建这样一个对象。默认的模型存储在对象PrOCoilModel。另一种模式，PrOCoilModelBA，优化平衡精度也可用（见下文）。对于未来的安全，其他机型可以装载从使用功能readCCModel文件。
判别函数模型----------Discriminant function of model----------
Given a new coiled coil sequence x and a model, the discriminant function of the model is given as
赋予了新的卷曲序列x和一个模型，该模型的判别函数是作为
where b is a constant, N(p,x) denotes the number of occurrences of pattern p in sequence x, and w(p) is the weight assigned to pattern p. P is the set of all patterns contained in the model. In the models used in the procoil package, the weights are computed from a support vector machine. Models can include kernel normalization or not. The formula above refers to the variant without kernel normalization. If kernel normalization is employed, the weights are computed in a different way and the discriminant function changes to
b是一个常数，N(p,x)p顺序x，w(p)是分配格局的重量表示的格局发生<X > p是在模型中包含的所有模式。 P包中所使用的模型，支持向量机的权重计算。模型可以包括内核或不标准化。上述公式是指变异没有内核标准化。如果采用内核标准化，以不同的方式计算重量和判别函数的变化
where R(x) is a normalization value depending on the sample x. It is defined as follows:
其中R(x)是一个标准化的价值取决于样品x。它的定义如下：
The procoil package does not consider arbitrary patterns, but only very specific ones: pairs of amino acids at fixed register positions with no more than a maximum number m of residues in between. Internally, these patterns are represented as strings with an amino acid letter on the first position, then a certain number of wildcards (between 0 and m as noted above), then the second amino acid letter, and finally a letter "a"-"g" denoting the heptad register position of the first amino acid, e.g. “N..La”. This pattern matches a coiled coil sequence if the sequence has an "N" (Asparagine) at an "a" position and a "L" (Leucine) at the next "d" position. For instance, the GCN4 wildtype has one occurrence of this pattern:
procoil包不考虑任意图案，但只有非常具体的：对在不超过最大数量m之间残留的固定寄存器位置的氨基酸。在内部，这些图案代表氨基酸信第一的位置上，然后一个通配符一定数量（介于0和m如上说明），然后第二个氨基酸的信，最后一个字母字符串; A-G表示七肽的氨基酸寄存器位置，如“N香格里拉”。这种模式相匹配的的卷曲顺序的序列，如果有“N”（天门冬）在一个a的位置和“L”在未来的“D”位置（亮氨酸）。例如，因子GCN4野生这种模式有一个发生：
插槽----------Slots----------
b: Object of class "numeric" the value
b：Object类的"numeric"值
m: Object of class "integer" the value
m：Object类的"integer"值
scaling: Object of class "logical" indicating whether the model should employ
scaling：Object类的"logical"表明该模型是否应该聘请
weights: Object of class "list" storing all pattern weights; the patterns are stored in the format
weights"list"储存所有模式的权重;格式存储模式：类对象
方法----------Methods----------
predict signature(object = "CCModel"): see
预测signature(object = "CCModel")：见
weights signature(object = "CCModel"): see
重signature(object = "CCModel")：见
show signature(object = "CCModel"): see
显示signature(object = "CCModel")：见
默认模式PrOCoilModel“----------Default model PrOCoilModel----------
The procoil package provides a default coiled coil prediction model, PrOCoilModel.
procoil包提供了一个默认的卷曲预测模型，PrOCoilModel。
The model was created with libSVM <CITE>[Chang and Lin, 2001]</CITE> using the coiled coil kernel with m=7, C=8, and kernel normalization on the BLAST-augmented data set. It is optimized for standard (unbalanced) accuracy, i.e. it tries to minimize the probability of misclassifications. Since dimers are more frequent in the data set, it slightly favors dimers for unknown sequences.
模型的创建与LIBSVM <CITE> [张和林，2001年] </引用>m=7，C=8，对高炉增强数据集的内核标准化卷曲内核。这是优化标准（不平衡）的精度，即它试图最大限度地减少错误分类概率。由于二聚体更频繁地在数据集，它稍微有利于对未知序列的二聚体。
替代模型PrOCoilModelBA----------Alternative model PrOCoilModelBA----------
As mentioned above, the default model PrOCoilModel slightly favors dimers. This may be undesirable for some applications. For such cases, an alternative model PrOCoilModelBA is available that is optimized for balanced accuracy, i.e. it tries not to favor the larger class - dimers -, but may therefore prefer trimers in borderline cases. The overall misclassification probability is slightly higher for this model than for the default model PrOCoilModel.
如上所述，默认模式PrOCoilModel稍微有利于二聚体。对于某些应用程序，这可能是不可取的。对于这样的情况下，替代模型PrOCoilModelBA平衡精度优化，即它试图不利于较大类 - 二聚体 - ，但可能会因此更喜欢模棱两可的情况中的三聚体。整体误判的概率是略微高于这一模式为默认模式PrOCoilModel。
The model PrOCoilModelBA was created with PSVM <CITE>[Hochreiter and Obermayer, 2006]</CITE> using the coiled coil kernel with m=8, C=2, e=1.3, class balancing, and kernel normalization on the BLAST-augmented data set.
模型PrOCoilModelBA创建PSVM <CITE> Hochreiter和Obermayer 2006] </引用>使用m=8，C=2，e=1.3，一流的卷曲内核高炉增强数据集上的平衡和内核标准化。
作者（S）----------Author(s)----------
Ulrich Bodenhofer <a href="mailto:bodenhofer@bioinf.jku.at">bodenhofer@bioinf.jku.at</a>
参考文献----------References----------
Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics. DOI: 10.1074/mcp.M110.004994
support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
dyadic data. Neural Computation 18:1472-1510. DOI: 10.1162/neco.2006.18.6.1472
参见----------See Also----------
predict-methods, show-methods,
predict-methods，show-methods
举例----------Examples----------
showClass("CCModel")
## show summary of default model (optimized for accuracy)[＃显示摘要（默认模型优化的准确性）]
PrOCoilModel
## show weight of pattern "N..La"[＃显示模式。香格里拉“N”的重量。]
weights(PrOCoilModel)[["N..La"]]
## show the 10 patterns that are most indicative for trimers[＃显示10个图案，三聚体的指示。]
## (as the weights are sorted in descending order in PrOCoilModel)[＃（重量在PrOCoilModel降序排列排序）]
weights(PrOCoilModel)[1:10]
## show the 10 patterns that are most indicative for dimers[＃显示10个图案，最二聚体的指示]
## (as the weights are sorted in descending order in PrOCoilModel)[＃（重量在PrOCoilModel降序排列排序）]
nW <- length(weights(PrOCoilModel))
weights(PrOCoilModel)[nW nW - 9)]
## predict oligomerization of GCN4 wildtype[＃预测因子GCN4野生齐聚]
GCN4wt<-predict(PrOCoilModel,
"MKQLEDKVEELLSKNYHLENEVARLKKLV",
"abcdefgabcdefgabcdefgabcdefga")
## show summary of alternative model (optimized for balanced accuracy)[＃显示摘要替代模式（平衡精度优化）]
PrOCoilModelBA
## show weight of pattern "N..La"[＃显示模式。香格里拉“N”的重量。]
weights(PrOCoilModelBA)[["N..La"]]
## show the 10 patterns that are most indicative for trimers[＃显示10个图案，三聚体的指示。]
## (as the weights are sorted in descending order in PrOCoilModelBA)[＃（为权重排序降序在PrOCoilModelBA秩序的）]
weights(PrOCoilModelBA)[1:10]
## show the 10 patterns that are most indicative for dimers[＃显示10个图案，最二聚体的指示]
## (as the weights are sorted in descending order in PrOCoilModelBA)[＃（为权重排序降序在PrOCoilModelBA秩序的）]
nW <- length(weights(PrOCoilModelBA))
weights(PrOCoilModelBA)[nWnW - 9)]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-26 11:29:42
