Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles.

ABSTRACT:

Objectives

Intra-uterine growth retardation is often of unknown origin, and is of great interest as a "Fetal Origin of Adult Disease" has been now well recognized. We built a benchmark based upon a previously analysed data set related to Intrauterine Growth Retardation with 46 subjects described by 14 variables, related with the insulin-like growth factor system and pro-inflammatory cytokines, namely interleukin-6 and tumor necrosis factor-α.

Design and methods

We used new algorithms for optimal information sorting based on the combination of two neural network algorithms: Auto-contractive Map and Activation and Competition System. Auto-Contractive Map spatializes the relationships among variables or records by constructing a suitable embedding space where 'closeness' among

SUBMITTER: Buscema M 

PROVIDER: S-EPMC4497659 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature