Necessary and sufficient conditions of proper estimators based on self density ratio for unnormalized statistical models.

The largest family of density-ratio based estimators is obtained for unnormalized statistical models under the assumption of properness. They do not require normalization of the probability density function (PDF) because they are based on the density ratio of the same PDF at different points; therefore, the multiplicative normalization constant cancels out. In contrast with most existing work, a single necessary and sufficient condition is given here, rather than merely sufficient conditions for proper criteria for estimation.

Selecting EEG components using time series analysis in brain death diagnosis.

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient's brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts.

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.

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.

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.