Classification Of Tissue Samples Research Articles

Clustering and classification based on the L 1 data depth

Clustering and classification are important tasks for the analysis of microarray gene expression data. Classification of tissue samples can be a valuable diagnostic tool for diseases such as cancer. Clustering samples or experiments may lead to the discovery of subclasses of diseases. Clustering genes can help identify groups of genes that respond similarly to a set of experimental conditions. We also need validation tools for clustering and classification. Here, we focus on the identification of outliers—units that may have been misallocated, or mislabeled, or are not representative of the classes or clusters. We present two new methods: DDclust and DDclass, for clustering and classification. These non-parametric methods are based on the intuitively simple concept of data depth. We apply the methods to several gene expression and simulated data sets. We also discuss a convenient visualization and validation tool—the relative data depth plot.

Machine learning in low-level microarray analysis

Machine learning and data mining have found a multitude of successful applications in microarray analysis, with gene clustering and classification of tissue samples being widely cited examples. Low-level microarray analysis -- often associated with the pre-processing stage within the microarray life-cycle -- has increasingly become an area of active research, traditionally involving techniques from classical statistics. This paper explores opportunities for the application of machine learning and data mining methods to several important low-level microarray analysis problems: monitoring gene expression, transcript discovery, genotyping and resequencing. Relevant methods and ideas from the machine learning community include semi-supervised learning, learning from heterogeneous data, and incremental learning.

Microcalorimetric measurements carried out on isolated tumorous and nontumorous tissue samples from organs in the urogenital tract in comparison to histological and impulse-cytophotometric investigations

In this comparative study, microcalorimetric measurements were carried out on a total of 96 tumorous and nontumorous tissue samples taken from organs of the urogenital tract using a thermal activity monitor (TAM). Changes in the heat emission of the tissue samples were measured at 1-min intervals and graphically displayed as a function of time. The aim of the study was to compare the microcalorimetric results with impulse-cytophotometric and histological findings and provide evidence for the metabolic activity of tumorous and nontumorous tissue. In order to obtain the variation in metabolic activity, the maxima (Pmax) of the curves were determined as a value of the maximum thermal power of a tissue sample, the mean values (P) were determined by the mean thermal power and the contour integrals (W) were defined by the behavior of the energy reserves and their mobilization. The first part of the study was carried out to investigate whether tumorous and nontumorous tissue samples differ in general according to their metabolic activity. We discovered, using the parameters described above, that in general tumorous tissue exhibited a higher metabolic activity than nontumorous tissue samples. For example, both W and P in tumorous prostate tissue samples were eightfold higher and the (Pmax) value was 8.4-fold higher than in normal tissue. Additional investigations on testicle and kidney tissues were performed to find a possible correlation between microcalorimetric results and histological grading. We found that an increasing malignancy correlated with a higher metabolic activity of the tissue. Based upon these results we were able to differentiate the various histological gradings of these tumorous tissues by microcalorimetric measurements. The results show it is possible to differentiate between normal and tumorous tissue samples by microcalorimetric measurement based on the distinctly higher metabolic activity of malignant tissue. Furthermore, microcalorimetry allows a differentiation and classification of tissue samples into their histological grading. With the help of microcalorimetry, it might be possible in future to detect and record the metabolic processes of isolated tissue structures and changes in these activities as a result of medical intervention such as cytostatic treatment.