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A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

MicroRNAs (miRNAs) are small noncoding RNA molecules thought to play an important role in regulating gene expression. Although knowledge of the biological functions of most miRNAs is still limited, miRNAs are thought to regulate the gene expression in various diseases and embryo development. In this chapter, the roles of miRNAs in human cancers are first examined from the viewpoint of up- and downregulation. Oncogenic miRNAs are involved in the overexpression/upregulation of cancers, whereas suppressive miRNAs are involved in the underexpression/downregulation of cancers. Statistical analysis of the positional nucleotide occurrence features of miRNAs revealed differences between the positional nucleotide occurrences of oncogenic and suppressive miRNAs. A miRNA gene-silencing score was then defined on the basis of the higher and lower levels of the statistical significances of positional nucleotides. Since the miRNA scores were closely related to miRNA frequencies, a method using the scores and nucleotide frequencies to distinguish whether a new miRNA is oncogenic or suppressive is proposed. This chapter also describes the roles of miRNAs in development. As miRNAs can act as cis-regulatory elements in the early embryonic development of Drosophila melanogaster, it is proposed that they mediate signal transduction between genes.

The relationships between Japanese Alzheimer's disease (AD) patients and their mitochondrial single nucleotide polymorphism (mtSNP) frequencies at individual mtDNA positions of the entire mitochondrial genome are described using the radial basis function (RBF) network and the modified method. Japanese AD patients are associated with the haplogroups G2a, B4c1, and N9b1. In addition, to compare mitochondrial haplogroups of the AD patients with those of other classes of Japanese people, the relationships between four classes of Japanese people (i.e., Japanese centenarians, Parkinson's disease (PD) patients, type 2 diabetic (T2D) patients, and non-obese young males) and their mtSNPs are also described. The four classes of people are associated with following haplogroups: Japanese centenarians-M7b2, D4b2a, and B5b; Japanese PD patients-M7b2, B4e, and B5b; Japanese T2D patients-B5b, M8a1, G, D4, and F1; and Japanese healthy non-obese young males-D4g and D4b1b. The haplogroups of the AD patients are therefore different from those of the other four classes of Japanese people. As the analysis method described in this article can predict a person's mtSNP constitution and the probabilities of becoming an AD patient, centenarian, PD patient, or T2D patient, it may be useful in initial diagnosis of various diseases.