Biomedical subjects

Shin Ishii

Publications and source records attributed to Shin Ishii.

3 recordsLinked to original sources

PubMed2003

Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data.

BACKGROUND: Individual human carcinomas have distinct biological and clinical properties: gene-expression profiling is expected to unveil the underlying molecular features. Particular interest has been focused on potential diagnostic and therapeutic applications. Solid tumors, such as colorectal carcinoma, present additional obstacles for experimental and data analysis. RESULTS: We analyzed the expression levels of 1,536 genes in 100 colorectal cancer and 11 normal tissues using adaptor-tagged competitive PCR, a high-throughput reverse transcription-PCR technique. A parametric clustering method using the Gaussian mixture model and the Bayes inference revealed three groups of expressed genes. Two contained large numbers of genes. One of these groups correlated well with both the differences between tumor and normal tissues and the presence or absence of distant metastasis, whereas the other correlated only with the tumor/normal difference. The third group comprised a small number of genes. Approximately half showed an identical expression pattern, and cancer tissues were classified into two groups by their expression levels. The high-expression group had strong correlation with distant metastasis, and a poorer survival rate than the low-expression group, indicating possible clinical applications of these genes. In addition to c-yes, a homolog of a viral oncogene, prognostic indicators included genes specific to glial cells, which gives a new link between malignancy and ectopic gene expression. CONCLUSIONS: The malignancy of human colorectal carcinoma is correlated with a unique expression pattern of a specific group of genes, allowing the classification of tumor tissues into two clinically distinct groups.

Cluster Analysis↗

PubMed1997

Chaotic Potts Spin Model for Combinatorial Optimization Problems.

In this paper we first show some of the bifurcation properties of Potts mean-field-theory annealing applied to traveling salesman problems. Due to these bifurcation properties, this approach, in general, produces non-optimal and non-unique solutions. As an alternative approach, we propose a nonequilibrium version of the Potts spin neural network, called chaotic Potts spin (CPS). CPS has several parameters, and bifurcations over each parameter are investigated. Next, experimental results are shown comparing CPS with several related approaches. CPS is good at obtaining optimal solutions for small-scale problems and semi-optimal solutions for relatively large-scale problems. We also describe a couple of CPS modifications: CPS with a heuristic method and CPS with a "chaotic annealing" method. These modified algorithms can produce even better CPS solutions. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

PubMedDate not supplied

Control of exploitation-exploration meta-parameter in reinforcement learning.

In reinforcement learning (RL), the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based RL, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corresponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [Science 283 (1999) 549] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain.

Animals↗