Soft topographic map for clustering and classification of bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification or erroneous annotations in the database

Feature Extraction with Weighted Samples Based on Independent Component Analysis

This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers. The method is a weighted version of our previous work based on the independent component analysis (ICA). In our previous work, ICA was applied to feature extraction for classification problems by including class information in the training. The resulting features contain much information on the class labels producing good classification performances. However, in many real world classification problems, it is hard to get a clean dataset and inherently, there may exist outliers or dubious data to complicate the learning process resulting in higher rates of misclassification. In addition, it is not unusual to find the samples with the same inputs to have different class labels. In this paper, Parzen window is used to estimate the correctness of the class information of a sample and the resulting class information is used for feature extraction.Y

Disadvantaged but different: variation among disadvantaged communities in relation to child and family well-being

Background: Disadvantaged communities are increasingly the target for interventions. Sure Start was launched in England in 1999 to tackle child poverty and improve child and family services, with Sure Start Local Programmes (SSLPs) targeted at relatively small areas of marked deprivation. However, they are located in a range of different types of communities where they may provide services to very different resident populations. They are all disadvantaged but underlying that label there are specific patterns of variability, relevant for service provision. To evaluate the implementation, impact, and cost‐effectiveness of SSLPs, or other area‐based initiatives, it is important to consider ways in which they can be grouped meaningfully according to these patterns. Method: Data were collected from administrative databases to describe SSLPs in terms of demography, deprivation, and aspects of child and family functioning and grouped using cluster analysis. Results: Five different ‘types’ of SSLP community were identified, based on their socio‐demographic and economic characteristics; typified by more, less or average deprivation in relation to all SSLPs, and in terms of the proportion of ethnic minority families resident in the areas. The groups differ in terms of community measures of child health, educational attainment, school disorder and child welfare and their prediction from demographic community characteristics. Conclusions: The groupings have implications for service delivery and the evaluation of area‐based initiatives