CCProfile-class(procoil)
CCProfile-class()所属R语言包：procoil
Class "CCProfile"
类“CCProfile”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
S4 class for representing coiled coil prediction results
中S4中类为代表卷曲预测结果
类的对象----------Objects from the Class----------
In principle, objects of this class can be created by calls of the form new("CCProfile"), although there is no need in doing so. Most importantly, the predict function of procoil stores its results in objects of this type.
的原则，在这个类的对象可以通过检测的形式new("CCProfile")创建，虽然目前还没有这样做的必要性。最重要的是，predictprocoil在这种类型的对象存储结果的功能。
插槽----------Slots----------
seq: Object of class "character" containing the amino acid sequence for which the
seq：Object类的"character"含有的氨基酸序列为
reg: Object of class "character" containing the heptad register corresponding to the amino acid sequence for which the
reg：对象类"character"包含七肽注册相应的氨基酸序列为
profile: Array of numerical values representing the prediction profile for the sequence under consideration. This array has the
profile：代表正在审议的顺序预测轮廓的数值阵列。这个数组
b: Object of class "numeric"; value b used in the discriminant function
b类"numeric"值对象; b用于判别函数
disc: Object of class "numeric" containing the discriminant function value
disc：Object类的"numeric"包含的判别函数值
pred: Object of class "character" containing the final classification. Upon a call to predict, it is either “trimer” or
pred类"character"包含最后的分类对象。调用predict黄飞鸿，它是“三聚”或
方法----------Methods----------
plot signature(x = "CCProfile", y = "missing"): see
图signature(x = "CCProfile", y = "missing")：见
plot signature(x = "CCProfile", y = "CCProfile"): see
图signature(x = "CCProfile", y = "CCProfile")：见
profile signature(fitted = "CCProfile"): see
个人资料signature(fitted = "CCProfile")：见
show signature(object = "CCProfile"):
显示signature(object = "CCProfile")：
预测概况----------Prediction profiles----------
As described in CCModel, the discriminant function of the coiled coil classifier is essentially a weighted sum of numbers of occurrences of certain patterns in the sequence under consideration, i.e. every pattern occurring in the sequence contributes a certain weight to the discriminant function. Since every such occurrence is uniquely linked to two specific residues in the sequence, every amino acid in the sequence contributes a unique weight to the discriminant function value which is nothing else but half the sum of weights of matching patterns in which this amino acid is involved. If we denote the contribution of each position i with si(x), it follows immediately that
作为CCModel，卷曲分类判别函数基本上是一个数字序列中的某些模式下审议中出现的加权总和，即每一个序列中发生的模式有助于在一定重量的判别函数。因为每一个这样的发生是唯一与两个序列中的特定残留，氨基酸序列中的每个贡献了独特的重量判别函数值，这是没有别的，但一半的匹配模式，在这种氨基酸是参与权的总和。如果我们表示每个位置的贡献i用si(x)，紧随其后
where L is the length of the sequence x.
其中L序列x的长度。
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.
参见----------See Also----------
CCModel, plot, plot, profile, show,
CCModel，plot，plot，profile，show
举例----------Examples----------
showClass("CCProfile")
## predict oligomerization of GCN4 wildtype[＃预测因子GCN4野生齐聚]
GCN4wt<-predict(PrOCoilModel,
"MKQLEDKVEELLSKNYHLENEVARLKKLV",
"abcdefgabcdefgabcdefgabcdefga")
## display summary of result[＃显示结果摘要]
GCN4wt
## show raw prediction profile[＃显示原预测的文件。]
profile(GCN4wt)
## plot profile[＃图轮廓]
plot(GCN4wt)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 11:29:49
