Biomedical subjects

Gen Hori

Publications and source records attributed to Gen Hori.

3 recordsLinked to original sources

PubMed2005

Gene expression profiling during the embryonic development of mouse brain using an oligonucleotide-based microarray system.

We analyzed gene expression profiles in embryonic day 12, 15, 18 and postnatal day 0 mouse brains by utilizing a GeneChip microarray. Significant differential expression was observed in 1413 of 12,422 (11.4%) represented on the chip. Then, 397 genes known to be related to neural development and functions were selected and analyzed in more detail. Clustering of the differentially expressed genes in terms of gene function and their temporal expression patterns indicated an aspect of the genetic foundation that underlies cellular events. Moreover, we identified a novel gene that encodes a putative protein kinase, Ebr kinase, which is differentially expressed in the developing brain.

Age Factors↗

PubMed2004

Improved parameter estimation for variance-stabilizing transformation of gene-expression microarray data.

A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outlier exclusion and works well even in the case of small sample size. Validation of this method with experimental data has suggested that it is superior to the conventional method.

Algorithms↗

PubMed2003

Gene interaction in DNA microarray data is decomposed by information geometric measure.

MOTIVATION: Given the vast amount of gene expression data, it is essential to develop a simple and reliable method of investigating the fine structure of gene interaction. We show how an information geometric measure achieves this. RESULTS: We introduce an information geometric measure of binary random vectors and show how this measure reveals the fine structure of gene interaction. In particular, we propose an iterative procedure by using this measure (called IPIG). The procedure finds higher-order dependencies which may underlie the interaction between two genes of interest. To demonstrate the method, we investigate the interaction between the two genes of interest in the data from human acute lymphoblastic leukemia cells. The method successfully discovered biologically known findings and also selected other genes as hidden causes that constitute the interaction. AVAILABILITY: Softwares are currently not available but are possibly made available in future at http://www.mns.brain.riken.go.jp/~nakahara/DNA_pub.html where all the related information is also linked.

Algorithms↗