BACKGROUND: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition. RESULTS: We compute the correlation between expression of a gene and amino acid composition of its protein. It was observed that some residues (like Ala, Gly, Arg and Val) have significant positive correlation (r > 0.20) and some other residues (Like Asp, Leu, Asn and Ser) have negative correlation (r < -0.15) with the expression of genes. A significant negative correlation (r = -0.18) was also found between length and gene expression. These observations indicate the relationship between percent composition and gene expression level. Thus, attempts have been made to develop a Support Vector Machine (SVM) based method for predicting the expression level of genes from its protein sequence. In this method the SVM is trained with proteins whose gene expression data is known in a given condition. Then trained SVM is used to predict the gene expression of other proteins of the same organism in the same condition. A correlation coefficient r = 0.70 was obtained between predicted and experimentally determined expression of genes, which improves from r = 0.70 to 0.72 when dipeptide composition was used instead of residue composition. The method was evaluated using 5-fold cross validation test. We also demonstrate that amino acid composition information along with gene expression data can be used for improving the function classification of proteins. CONCLUSION: There is a correlation between gene expression and amino acid composition that can be used to predict the expression level of genes up to a certain extent. A web server based on the above strategy has been developed for calculating the correlation between amino acid composition and gene expression and prediction of expression level http://kiwi.postech.ac.kr/raghava/lgepred/. This server will allow users to study the evolution from expression data.

In this paper, a new fuzzy K-nearest neighbor (K-NN) algorithm, called "Variance Weighted Fuzzy K-NN", is proposed. The main idea of this method is in giving weights to neighbors according to the standard deviation of their class membership values which reflect the value of a discriminant function. The classification results of 32 classes of complex images are given. Compared to the K-NN and fuzzy K-NN algorithms, our method shows an improved classification rate for various conditions.

The authors propose a method of statistical visual pattern recognition with an optimum organizational feature set which can be applied to the classification of complex 2D shapes. This method can be applied to the classification of complicated patterns present on the surfaces of materials. The advantages of this method come from the organizational feature set, which partitions a pattern vector in such a way as to minimize the loss of information caused by the partitioning, and from the paradigmatic representations of object classes, which contain probabilities of all states of the feature vectors of the classes. Classification performance showed that the proposed method is superior to the method which uses randomly selected features.>

A simple and reliable method is presented for detecting concave and convex discontinuous points in plane curves. The method can be applied to detect feature points in 2-D binary images or it can be used in detecting surface boundaries in range images. The author also shows that the impulse response of the detection method is similar to that of the second derivative of a Gaussian operator. Some experimental results are given with 2-D images and range images.>

A novel method is presented for grouping range image regions such that each group of regions represents a meaningful part of an object. The set of regions, defined as a convex region set (CRS), is obtained by analyzing the boundary types between a pair of regions. The boundary types are classified as convex, concave and jump boundaries. If two regions share a convex boundary it is assumed that they are inseparable regions, thus describing the same part (object). The CRSs are determined by a region boundary graph (RBG) which is defined as a graph whose nodes represent regions, and the edges represent boundaries: convex and concave. Since jump boundaries represent no physical contact in 3-D, they are represented as null edges. A CRS is defined as a set of regions (or nodes in an RBG) such that for each pair of regions in the set, there is a path, which is represented only by convex edges. The physical interpretation is that a CRS represents part of an object such that the regions in the set cannot be separated.>