Biomedical subjects

Reiji Teramoto

Publications and source records attributed to Reiji Teramoto.

3 recordsLinked to original sources

PubMed2005

A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings.

Alzheimer's disease (AD) is a neurodegenerative disease with an insidious onset and progressive course that inevitably leads to death. The current diagnostic tools do not allow for diagnosis until the disease has lead to irreversible brain damage. Genetic studies of autosomal dominant early onset familial AD has identified three causative genes: amyloid precursor protein (APP), presenilin 1 and 2 (PSEN1 and PSEN2). We performed a global gene expression analysis on fibroblasts from 33 individuals (both healthy and demented mutation carriers as well as wild-type siblings) from three families segregating the APP(SWE), APP(ARC) and PSEN1 H163Y mutations, respectively. The mutations cause hereditary progressive cognitive disorder, including typical autosomal dominant AD. Our data show that the mutation carriers share a common gene expression profile significantly different from that of their wild-type siblings. The results indicate that the disease process starts several decades before the onset of cognitive decline, suggesting that presymptomatic diagnosis of AD and other progressive cognitive disorders may be feasible in the near future.

Alzheimer Disease↗

PubMed2005

Prediction of siRNA functionality using generalized string kernel and support vector machine.

Small interfering RNAs (siRNAs) are becoming widely used for sequence-specific gene silencing in mammalian cells, but designing an effective siRNA is still a challenging task. In this study, we developed an algorithm for predicting siRNA functionality by using generalized string kernel (GSK) combined with support vector machine (SVM). With GSK, siRNA sequences were represented as vectors in a multi-dimensional feature space according to the numbers of subsequences in each siRNA, and subsequently classified with SVM into effective or ineffective siRNAs. We applied this algorithm to published siRNAs, and could classify effective and ineffective siRNAs with 90.6%, 86.2% accuracy, respectively.

Algorithms↗

PubMed2004

A method for clustering gene expression data based on graph structure.

Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relationships of genes as a tree structure by connecting genes using their similarity scores based on the Pearson correlation coefficient. But the clustering method is sensitive to experimental noise. To cope with the problem, we propose another type of clustering method (the p-quasi complete linkage clustering). We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods.

Algorithms↗