Keon Myung Lee

Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer

BackgroundWhile several molecular markers of bladder cancer prognosis have been identified, the limited value of current prognostic markers has created the need for new molecular indicators of bladder cancer outcomes. The aim of this study was to identify genetic signatures associated with disease prognosis in bladder cancer.ResultsWe used 272 primary bladder cancer specimens for microarray analysis and real-time reverse transcriptase polymerase chain reaction (RT-PCR) analysis. Microarray gene expression analysis of randomly selected 165 primary bladder cancer specimens as an original cohort was carried out. Risk scores were applied to stratify prognosis-related gene classifiers. Prognosis-related gene classifiers were individually analyzed with tumor invasiveness (non-muscle invasive bladder cancer [NMIBC] and muscle invasive bladder cancer [MIBC]) and prognosis. We validated selected gene classifiers using RT-PCR in the original (165) and independent (107) cohorts. Ninety-seven genes related to disease progression among NMIBC patients were identified by microarray data analysis. Eight genes, a progression-related gene classifier in NMIBC, were selected for RT-PCR. The progression-related gene classifier in patients with NMIBC was closely correlated with progression in both original and independent cohorts. Furthermore, no patient with NMIBC in the good-prognosis signature group experienced cancer progression.ConclusionsWe identified progression-related gene classifier that has strong predictive value for determining disease outcome in NMIBC. This gene classifier could assist in selecting NMIBC patients who might benefit from more aggressive therapeutic intervention or surveillance.

Similarity measure between fuzzy sets and between elements

Abstract Two similarity measures are proposed: one for the similarity between fuzzy sets and the other between elements in fuzzy sets. With and example, it is shown that the proposed measures can be used in the behavior analysis in an organization.

A genetic algorithm for general machine scheduling problems

This paper deals with the so-called general machine scheduling problems. In the general machine scheduling problems, job shop type jobs and open shop type jobs are scheduled together and the imposition of precedence constraints is allowed between operations belonging to either the same job or different jobs. This paper proposes a genetic algorithm to solve such general machine scheduling problems. Some experimental results are presented to show the applicability of the proposed method. The method can be used to solve traditional job shop scheduling, flow shop scheduling, and open shop scheduling as well as general machine scheduling problems.

Ranking fuzzy values with satisfaction function

Abstract This paper proposes a measure called the satisfaction function which estimates the satisfaction degree of arithmetic comparison relations (such as >,

Enhanced Expression of Peroxiredoxin I and VI Correlates with Development, Recurrence and Progression of Human Bladder Cancer

PURPOSE PRDXs are antioxidant enzymes that have an important role in cell differentiation, proliferation and apoptosis. We investigated whether PRDX I and VI expression is related to bladder cancer. MATERIALS AND METHODS PRDX I and VI mRNA levels were examined in 149 tumor specimens in patients with primary bladder cancer, in 19 specimens with corresponding normal-appearing bladder mucosa surrounding cancer and in 18 with normal bladder mucosa using real-time polymerase chain reaction. RESULTS PRDX I and VI expression in bladder cancer (0.6644 and 0.1455 pg/ml) was significantly higher than in normal tissue (0.0278 and 0.0542 pg/ml, each p <0.05) and higher than in corresponding normal bladder mucosa surrounding cancer (0.2353 and 0.0304 pg/ml, respectively, each p <0.0005). PRDX I and VI expression was enhanced in patients with no recurrence (0.8148 and 0.2232 pg/ml) and no progression (0.7405 and 0.1716 pg/ml) compared with levels in those with recurrence (0.4314 and 0.0588 pg/ml) and progression (0.4338 and 0.0668 pg/ml, respectively, each p <0.05). PRDX I and VI expression did not correlate with disease-free survival in patients with bladder cancer. CONCLUSIONS Enhanced PRDX I and VI expression is strongly associated with bladder cancer development. Moreover, enhanced PRDX I and VI expression is also positively associated with a low rate of bladder cancer recurrence and progression. It might be useful as a marker for assessing the recurrence or progression of human bladder cancer.

Identification of l-fuzzy measure by genetic algorithms

Fuzzy measures is a measure for representing the membership degrees of an object to candidate sets. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjectively determined. Thus it induces an identification problem that determines measure values with fuzzy measure properties from human-provided measure values. The λ-fuzzy measure is a typical fuzzy measure widely used. Several methods have been developed for λ-fuzzy measure identification. Such methods, however, have restrictions on data set used in the identification, or require complicate computation, and thus not easy to use. Therefore, this paper proposes a λ-fuzzy measure identification method based on genetic algorithms, and shows its applicability by some experiments.

Comparison of Interval-valued fuzzy sets, Intuitionistic fuzzy sets, and bipolar-valued fuzzy sets

There are several kinds of fuzzy set extensions in the fuzzy set theory. Among them, this paper is concerned with interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets. In interval-valued fuzzy sets, membership degrees are represented by an interval value that reflects the uncertainty in assigning membership degrees. In intuitionistic fuzzy sets, membership degrees are described with a pair of a membership degree and a nonmembership degree. In bipolar-valued fuzzy sets, membership degrees are specified by the satisfaction degrees to a constraint and its counter-constraint. This paper investigates the similarities and differences among these fuzzy set representations.

Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining for taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies for categorical attributes and generalization hierarchies of fuzzy linguistic terms for quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights for attributes.

Fuzzy hypergraph and fuzzy partition

In this paper, the concept of hypergraph is extended to the fuzzy hypergraph. In the fuzzy hypergraph, the concepts of /spl alpha/-cut hypergraph, strength of edge and dual fuzzy hypergraph are developed. It is shown that the fuzzy hypergraph and /spl alpha/-cut hypergraph are useful to represent a fuzzy partition. An application example also shows that the strength of edge can be used to decompose the data set in a clustering problem. >

Tuning of fuzzy models by fuzzy neural networks

It is relatively easy to construct a rough fuzzy model with expert knowledge. It is difficult, however, to fine-tune the parameters of the fuzzy model in order to get improved behavior. For the purpose of tackling this problem, we propose a fuzzy neural network model. The proposed model utilizes a prior expert knowledge for target systems, and embodies fuzzy models which consist of fuzzy rules whose antecedent and consequent are fuzzy sets. The model is equipped with a fuzzy inferencing and tuning mechanism for model parameters by learning. It allows us to tune such parameters of fuzzy models as linguistic terms and relative rule importance. In addition, to show its applicability, we perform some experiments and present the results.