Neural Network Signal Analysis in Immunology

This paper aims to investigate whether both supervised and unsupervised signal analysis contributes to the interpretation of immunological data. For this purpose a data base was set up containing measured data from bronchoalveolarlavage fluid which was obtained from 37 children with pulmonary diseases. The children were dichotomized into two groups: 20 children suffered from chronic bronchitis whereas 17 children had an interstitial lung disease. A self-organizing map (SOM) was utilized to test higher-order correlations between cellular subsets and the patient groups. Furthermore, a supervised approach with a perceptron trained to the patients' diagnosis was applied. The SOM confirmed the results that were expected from previous statistical analyses and shed light on formerly not considered relationships. The supervised perceptron learning after principal component analysis for dimension reduction turned out to be highly successful by linearly separating the patients into two groups with different diagnoses. The simplicity of the perceptron made it easy to extract diagnosis rules, which partly were known already and is now readily be tested on larger data sets

Neural Network Signal Analysis In Immunology

This paper aims to investigate whether both supervised and unsupervised signal analysis can contribute to the interpretation of immunological data. For this purpose a data base was set up containing measured data from bronchoalveolar lavage fluid which was obtained from 37 children with pulmonary diseases. The children were dichotomized into two groups: 20 children suffered from chronic bronchitis whereas 17 children had an interstitial lung disease. A self-organizing map (SOM) was utilized to test higherorder correlations between cellular subsets and the patient groups. Furthermore, a supervised approach with a perceptron trained to the patients' diagnosis was applied. The SOM confirmed the results that were expected from previous statistical analyses and shed light on formerly not considered relationships. The supervised perceptron learning after principal component analysis for dimension reduction turned out to be highly successful by linearly separating the patients into two groups with different diagnoses. The simplicity of the perceptron made it easy to extract diagnosis rules, which partly were known already and can now readily be tested on larger data sets