Perspective most of the data we obtained from our previous study, comparing IUGR and AGA newborns [4,13,16]. Fourteen variables were selected for the analysis from the entire database of 46 subjects: IUGR and AGA membership, gender, gestational age, total placental protein content per mg of placental tissue (PRO g/mg), relative gene expression for IGF-I, IGF-2, IGFBP-1, IGFBP-2, and IL-6, (abbreviated as mRNA_IGF1, mRNA_IGF2, mRNA-BP1, mRNA_BP2, mRNA_IL6, respectively), and placental lysate content in IGF-2, IGFBP-2, TNF-, and IL-6 (abbreviated as: PLA_IGF2, PLA_BP2, PLATNF, and PLAIL6 respectively).Basic StatisticsThe linear correlation index between variables was calculated. Simple Student’s-T test was used to compare R squared between each variable and IUGR and AGA targets in the two groups of variables.Classic AlgorithmsDifferent algorithms were applied to the dataset and its results compared with the real class to which each subject belonged: i) K-mean clustering was performed according the method described by Rousseeuw [19] (in short K-Mean); ii) Minimum Spanning Tree (MST) Clustering based on Linear Correlation (in short LC MST); iii) Principal Component Analysis (PCA) was applied on the dataset (implementation from MatLab ToolBox) and then its two mainPLOS ONE | DOI:10.1371/journal.pone.0126020 July 9,4 /Data Mining of Determinants of IUGRcomponents were post-processed with the Minimum Spanning Tree (in short PCA MST); iv) Linear Discriminant Analysis (LDA) based on the input generated by PCA (in short PCA-LDA); v) Self Organizing Maps (SOM) with a matrix 10×10 run for 100 epochs (software implementation by Matlab ToolBox) and filtered by MST (in short SOM MST); vi) LDA based on SOM codebooks (in short SOM LDA). As LDA is a MK-5172 structure supervised algorithm we used the Leave One Out protocol to evaluate the results. In this way we applied the algorithm on the whole sample.Artificial Neural Networks AnalysisWe subsequently used new and powerful ANNs: i) (Auto Contractive Map) AutoCM, a new non-linear ANN designed in 1999 by M. Buscema at the Semeion Research Center. AutoCM algorithm was previously applied in medicine with very interesting results [20?3]; ii) (Activation and Competition System) ACS, a new non-linear Auto Associative Grazoprevir site Memory, created by M. Buscema at the Semeion Research Center [24]. The theories and mathematical details of the two ANNs are described in detail below.AutoCM Artificial Neural NetworkAutoCM `spatializes’ the correlation among variables by building a suitable embedding space where a visually transparent and cognitively natural notion such as `closeness’ among variables reflects accurately their associations. AutoCM converts this `closeness’ into a compelling graph-theoretical representation that picks all and only the relevant correlations and organizes them into a coherent picture. Such representation is not actually built through a cumbersome aggregation of two-by-two associations between couples of variables, but rather by building a complex global picture of the whole pattern of variation. Moreover, it fully exploits the topological meaning of graph-theoretical representations in that actual paths connecting vertices (variables) in the representation carry a definite meaning in terms of logical interdependence in explaining the data set’s variability. The AutoCM is characterized by a three-layer architecture: an Input layer, where the signal is captured from the environment, a Hidden layer, where the signal is.Perspective most of the data we obtained from our previous study, comparing IUGR and AGA newborns [4,13,16]. Fourteen variables were selected for the analysis from the entire database of 46 subjects: IUGR and AGA membership, gender, gestational age, total placental protein content per mg of placental tissue (PRO g/mg), relative gene expression for IGF-I, IGF-2, IGFBP-1, IGFBP-2, and IL-6, (abbreviated as mRNA_IGF1, mRNA_IGF2, mRNA-BP1, mRNA_BP2, mRNA_IL6, respectively), and placental lysate content in IGF-2, IGFBP-2, TNF-, and IL-6 (abbreviated as: PLA_IGF2, PLA_BP2, PLATNF, and PLAIL6 respectively).Basic StatisticsThe linear correlation index between variables was calculated. Simple Student’s-T test was used to compare R squared between each variable and IUGR and AGA targets in the two groups of variables.Classic AlgorithmsDifferent algorithms were applied to the dataset and its results compared with the real class to which each subject belonged: i) K-mean clustering was performed according the method described by Rousseeuw [19] (in short K-Mean); ii) Minimum Spanning Tree (MST) Clustering based on Linear Correlation (in short LC MST); iii) Principal Component Analysis (PCA) was applied on the dataset (implementation from MatLab ToolBox) and then its two mainPLOS ONE | DOI:10.1371/journal.pone.0126020 July 9,4 /Data Mining of Determinants of IUGRcomponents were post-processed with the Minimum Spanning Tree (in short PCA MST); iv) Linear Discriminant Analysis (LDA) based on the input generated by PCA (in short PCA-LDA); v) Self Organizing Maps (SOM) with a matrix 10×10 run for 100 epochs (software implementation by Matlab ToolBox) and filtered by MST (in short SOM MST); vi) LDA based on SOM codebooks (in short SOM LDA). As LDA is a supervised algorithm we used the Leave One Out protocol to evaluate the results. In this way we applied the algorithm on the whole sample.Artificial Neural Networks AnalysisWe subsequently used new and powerful ANNs: i) (Auto Contractive Map) AutoCM, a new non-linear ANN designed in 1999 by M. Buscema at the Semeion Research Center. AutoCM algorithm was previously applied in medicine with very interesting results [20?3]; ii) (Activation and Competition System) ACS, a new non-linear Auto Associative Memory, created by M. Buscema at the Semeion Research Center [24]. The theories and mathematical details of the two ANNs are described in detail below.AutoCM Artificial Neural NetworkAutoCM `spatializes’ the correlation among variables by building a suitable embedding space where a visually transparent and cognitively natural notion such as `closeness’ among variables reflects accurately their associations. AutoCM converts this `closeness’ into a compelling graph-theoretical representation that picks all and only the relevant correlations and organizes them into a coherent picture. Such representation is not actually built through a cumbersome aggregation of two-by-two associations between couples of variables, but rather by building a complex global picture of the whole pattern of variation. Moreover, it fully exploits the topological meaning of graph-theoretical representations in that actual paths connecting vertices (variables) in the representation carry a definite meaning in terms of logical interdependence in explaining the data set’s variability. The AutoCM is characterized by a three-layer architecture: an Input layer, where the signal is captured from the environment, a Hidden layer, where the signal is.
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