Buscema Massimo, Grossi Enzo
Semeion Research Centre of Sciences of Communication, Via Sersale 117-CP 00128, Rome, Italy.
Int J Data Min Bioinform. 2008;2(4):362-404. doi: 10.1504/ijdmb.2008.022159.
We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re-design the overall structure of factors related to complex and specific diseases description.
我们在此描述一种新的映射方法，借助人工自适应系统——自动收缩映射（AutoCM），能够找出变量之间的连通性轨迹，该系统能够定义数据集中每个变量与其他所有变量的关联强度。在训练阶段之后，AutoCM的权重矩阵代表了变量之间主要连接的映射。胃食管反流病数据库的例子对于弄清楚这种新方法如何有助于重新设计与复杂和特定疾病描述相关的因素的整体结构非常有用。
